Search results for: robotic vision

Authors: Rupali Satsangi

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14 years ago, leaders from every country agreed on a vision for the future – a world with less poverty, hunger and disease, greater survival prospects for mothers and their infants, better educated children, equal opportunities for women, and a healthier environment; a world in which developed and developing countries work in partnership for the betterment of all. This vision took the shape of eight Millennium Development Goals, which provide countries around the world a framework for development and time-bound targets by which progress can be measured. However, India has found 35 of the indicators as relevant to India. India’s MDG-framework has been contextualized through a concordance with the existing official indicators of corresponding dimensions in the national statistical system. The present study based on secondary data analyzed the status of India towards achieving the MDGs after reviewing the data study find out that India can miss the MDGs Bus in women health, sanitation and global partnership. These goals were less addressed by India in his policies and takeoffs.

Keywords: millennium development goals, national statistical system, global partnership, healthier environment

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Authors: Tillie Curran

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This co-production project aimed for an expansive exploration of disabled young people’s hopes and dreams in the context of unprecedented societal changes. The research questions developed with disabled young people acting as peer researchers, ask ‘what does a good life look like now, and, what are your hopes and dreams for the future?’ Disabled children’s childhood studies and an asset-based approach placed the voice of disabled young people at the centre of the research process and inviting participants to ‘think big’! Over 18 months, academics, members of a Centre for Independent Living and peer researchers, came together to facilitate knowledge cafes with fifty disabled young people aged between 14 and 25 in a college and youth club setting. Methods used included trigger questions, photos voice, video, and cartooning. The peer researchers also investigated how house robots and connected autonomous vehicles might support their future aspirations and sense of freedom in this new era with a trip to the university robotic laboratory. Key themes arising from participants’ hopes and dream were about ‘being responsible’, ‘loving’, ‘freedom and happiness’ and a ‘strong sense of self and togetherness’ and suggest alternative narratives and rich visions of the future possibilities for disabled young people. The five key messages peer researchers produced for the report emphasised freedom to define their futures, desires to make the world a better place, to belong and have the chance of their own family life. Thematic analysis, production of the report and impact activities were all co-produced and as the project progressed peer researchers increasingly demonstrated a role as ‘change makers’ and have formed a young people’s co-production group going on into the future. Discussion of the project highlights the factors that made these processes successful and the ethical dilemmas encountered in the context of normalcy. Finally, we consider the implications for all involved as we rethink ‘the future’, not in terms of normative ideals or trajectories, or seeing service ‘transition’ as an end, but in terms of disabled young people’s contribution, participation, freedoms, and possibilities.

Keywords: co-production, disability, robotic, youth

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Authors: Shyam Mehraaj, Samanthi E. R. Siriwardana, Shehara A. K. G. H., Wanigasinghe N. T., Wandana R. A. K., Wedage C. V.

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A presentation is a critical tool for conveying not just spoken information but also a wide spectrum of human emotions. The single most effective thing to make the presentation successful is to practice it beforehand. Preparing for a presentation has been shown to be essential for improving emotional control, intonation and prosody, pronunciation, and vocabulary, as well as the quality of the presentation slides. As a result, practicing has become one of the most critical parts of giving a good presentation. In this research, the main focus is to analyze the audio, video, and slides of the presentation uploaded by the presenters. This proposed solution is based on the Natural Language Processing and Computer Vision techniques to cater to the requirement for the presenter to do a presentation beforehand using a mobile responsive web application. The proposed system will assist in practicing the presentation beforehand by identifying the presenters’ emotions, body language, tonality, prosody, pronunciations and vocabulary, and presentation slides quality. Overall, the system will give a rating and feedback to the presenter about the performance so that the presenters’ can improve their presentation skills.

Keywords: presentation, self-evaluation, natural learning processing, computer vision

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Authors: Hanaa M. Abdelgawad, Mona Safar, Ayman M. Wahba

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Real-time image and video processing is a demand in many computer vision applications, e.g. video surveillance, traffic management and medical imaging. The processing of those video applications requires high computational power. Therefore, the optimal solution is the collaboration of CPU and hardware accelerators. In this paper, a Canny edge detection hardware accelerator is proposed. Canny edge detection is one of the common blocks in the pre-processing phase of image and video processing pipeline. Our presented approach targets offloading the Canny edge detection algorithm from processing system (PS) to programmable logic (PL) taking the advantage of High Level Synthesis (HLS) tool flow to accelerate the implementation on Zynq platform. The resulting implementation enables up to a 100x performance improvement through hardware acceleration. The CPU utilization drops down and the frame rate jumps to 60 fps of 1080p full HD input video stream.

Keywords: high level synthesis, canny edge detection, hardware accelerators, computer vision

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Authors: Upal Barua

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The risk of severe crashes at high-speed right-turns at intersections is a major safety concern these days. The application of a smart right-turn at an intersection is increasing day by day to address is an issue. The design, ‘Smart Right-turn’ consists of a narrow-angle of channelization at approximately 70°. This design increases the cone of vision of the right-tuning drivers towards the crossing pedestrians as well as traffic on the cross-road. As part of the Safety Improvement Program in Austin Transportation Department, several smart right-turns were constructed at high crash intersections where high-speed right-turns were found to be a contributing factor. This paper features the state of the art techniques applied in planning, engineering, designing and construction of this smart right-turn, key factors driving the success, and lessons learned in the process. This paper also presents the significant crash reductions achieved from the application of this smart right-turn design using Empirical Bayes method. The result showed that smart right-turns can reduce overall right-turn crashes by 43% and severe right-turn crashes by 70%.

Keywords: smart right-turn, intersection, cone of vision, empirical Bayes method

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Authors: Azza Taha Zaki

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The death of tragedy is probably the most distinctive literary controversy of the twentieth century. There is common critical consent that tragedy in the classical sense of the word is no longer possible. Thinkers, philosophers, and critics such as Nietzsche, Durrenmatt, and George Steiner have all agreed that the decline of the genre in the modern age is due to the total lack of a unified world image and the absence of a shared vision in a fragmented and ideologically diversified world. The production of Rosencrantz and Guildenstern are Dead in 1967 marked the rise of the genre of tragi-comedy as a more appropriate reflection of the spirit of the age. At the hands of such great dramatists as Tom Stoppard (1937- ), the revived genre was not used as an extra comic element to give some comic relief to an otherwise tragic text, but it was given a postmodernist touch to serve the interpretation of the dilemma of man in the postmodernist world. This paper will study features of postmodernist tragi-comedy in Rosencrantz and Guildenstern are Dead as one of the most important plays in modern British theatre and investigate Stoppard’s vision of man and life as influenced by postmodernist thought and philosophy.

Keywords: British, drama, postmodernist, Stoppard, tragi-comedy

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Authors: A. Abbasi Moshaii, M. Soltan Rezaee, M. Mohammadi Moghaddam

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Control of some mechanisms is hard because of their complex dynamic equations. If part of the complexity is resulting from uncertainties, an efficient way for solving that is robust control. By this way, the control procedure could be simple and fast and finally, a simple controller can be designed. One kind of these mechanisms is 3-RRR which is a parallel mechanism and has three revolute joints. This paper aims to robust control a 3-RRR planner mechanism and it presents that this could be used for other mechanisms. So, a significant problem in mechanisms control could be solved. The relevant diagrams are drawn and they show the correctness of control process.

Keywords: 3-RRR, dynamic equations, mechanisms control, structural uncertainty

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Hengameh Hosseini

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Design of large healthcare facilities – such as hospitals, multi-service line clinics, and nursing facilities - that can accommodate patients with wide-ranging disabilities is a challenging endeavor and one that is poorly understood among healthcare facility managers, administrators, and executives. An even less-understood extension of this problem is the implications of weakly or insufficiently accommodative design of facilities for healthcare workers in physically-intensive jobs who may also suffer from a range of disabilities and who are therefore at increased risk of workplace accident and injury. Combine this reality with the vast range of facility types, ages, and designs, and the problem of universal accommodation becomes even more daunting and complex. In this study, we focus on the implication of facility design for healthcare workers suffering with low vision who also have physically active jobs. The points of difficulty are myriad and could span health service infrastructure, the equipment used in health facilities, and transport to and from appointments and other services can all pose a barrier to health care if they are inaccessible, less accessible, or even simply less comfortable for people with various disabilities. We conduct a series of surveys and interviews with employees and administrators of 7 facilities of a range of sizes and ownership models in the Northeastern United States and combine that corpus with in-facility observations and data collection to identify five major points of failure common to all the facilities that we concluded could pose safety threats to employees with vision impairments, ranging from very minor to severe. We determine that lack of design empathy is a major commonality among facility management and ownership. We subsequently propose three methods for remedying this lack of empathy-informed design, to remedy the dangers posed to employees: the use of an existing open-sourced Augmented Reality application to simulate the low-vision experience for designers and managers; the use of a machine learning model we develop to automatically infer facility shortcomings from large datasets of recorded patient and employee reviews and feedback; and the use of a computer vision model fine tuned on images of each facility to infer and predict facility features, locations, and workflows, that could again pose meaningful dangers to visually impaired employees of each facility. After conducting a series of real-world comparative experiments with each of these approaches, we conclude that each of these are viable solutions under particular sets of conditions, and finally characterize the range of facility types, workforce composition profiles, and work conditions under which each of these methods would be most apt and successful.

Keywords: artificial intelligence, healthcare workers, facility design, disability, visually impaired, workplace safety

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Authors: Merav Hayakac, Orit Avidov-Ungarab

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The current study adopted an interpretive-constructivist approach to examine how senior academics from a large sample of Israeli teacher education colleges serving general or religious populations perceived the integration of digital games into their teacher instruction and what their policy and vision were in this regard in the context of the COVID-19 pandemic. Half the participants expressed a desire to integrate digital games into their teaching and learning but acknowledged that this practice was uncommon. Only a small minority believed they had achieved successful integration, with doubt and skepticism expressed by some religious colleges. Most colleges had policies encouraging technology integration supported by ongoing funding. Although a considerable gap between policy and implementation remained, the COVID-19 pandemic was viewed as having accelerated the integration of digital games into pre-service teacher instruction. The findings suggest that discussions around technology-related vision and policy and their translation into practice should relate to the specific cultural needs and academic preparedness of the population(s) served by the college.

Keywords: COVID-19, digital games, pedagogy, teacher education colleges

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Authors: Xingdu Wang, Charlie Gere, Emma Rose, Yuxuan Zhao

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The study introduces a fresh perspective on the digital realm through an examination of the Chinese concept of Xiang, elucidating how it can build an understanding of pixels and textures on screens as digital trigrams. This concept attempts to offer an outlook on the intersection of digital technology and the natural world, thereby contributing to discussions about the harmonious relationship between humans and technology. The study looks for the ancient Chinese theory of Xiang as a key to establishing the theories and practices to respond to the problem of Contemporary Chinese technics. Xiang is a Chinese method of understanding the essentials of things through appearances, which differs from the method of science in the Westen. Xiang, the basement of Chinese visual art, is rooted in ancient Chinese philosophy and connected to the eight trigrams. The discussion of Xiang connects art, philosophy, and technology. This paper connects the meaning of Xiang with the 'truth appearing' philosophically through the analysis of the concepts of phenomenon and noumenon and the unique Chinese way of observing. Hereafter, the historical interconnection between ancient painting and writing in China emphasizes their relationship between technical craftsmanship and artistic expression. In digital, the paper blurs the traditional boundaries between images and text on digital screens in theory. Lastly, this study identified an ensemble concept relating to pixels and textures in computer vision, drawing inspiration from AI image recognition in Chinese paintings. In art practice, by presenting a fluid visual experience in the form of pixels, which mimics the flow of lines in traditional calligraphy and painting, it is hoped that the viewer will be brought back to the process of the truth appearing as defined by the 'Xiang’.

Keywords: Chinese cosmotechnics, computer vision, contemporary Neo-Confucianism, texture and pixel, Xiang

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Authors: Promise Ekpo Osaine, Daniel Melesse

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Image captioning is an active area of research in the multi-modal artificial intelligence (AI) community as it connects vision and language understanding, especially in settings where it is required that a model understands the content shown in an image and generates semantically and grammatically correct descriptions. In this project, we followed a standard approach to a deep learning-based image captioning model, injecting architecture for the encoder-decoder setup, where the encoder extracts image features, and the decoder generates a sequence of words that represents the image content. As such, we investigated image encoders, which are ResNet101, InceptionResNetV2, EfficientNetB7, EfficientNetV2M, and CLIP. As a caption generation structure, we explored long short-term memory (LSTM). The CLIP-LSTM model demonstrated superior performance compared to the encoder-decoder models, achieving a BLEU-1 score of 0.904 and a BLEU-4 score of 0.640. Additionally, among the CNN-LSTM models, EfficientNetV2M-LSTM exhibited the highest performance with a BLEU-1 score of 0.896 and a BLEU-4 score of 0.586 while using a single-layer LSTM.

Keywords: multi-modal AI systems, image captioning, encoder, decoder, BLUE score

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Authors: Aline Giardin, Maria Rosa Chitolina, Maria Catarina Zanini

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In this paper, we analyze the conceptions of high school students about mental health issues, and discuss the creation of mental basic health programs in schools. We base our findings in a quantitative survey carried out by us with 156 high school students of CTISM (Colégio Técnico Industrial de Santa Maria) school, located in Santa Maria city, Brazil. We have found that: (a) 28 students relate the subject ‘mental health’ with psychiatric hospitals and lunatic asylums; (b) 28 students have relatives affected by mental diseases; (c) 76 students believe that mental patients, if treated, can live a healthy life; (d) depression, schizophrenia and bipolar disorder are the most cited diseases; (e) 84 students have contact with mental patients, but know nothing about the disease; (f) 123 students have never been instructed about mental diseases while in the school; and (g) 135 students think that a mental health program would be important in the school. We argue that these numbers reflect a vision of mental health that can be related to the reductionist education still present in schools and to the lack of integration between health professionals, sciences teachers, and students. Furthermore, this vision can also be related to a stigmatization process, which interferes with the interactions and with the representations regarding mental disorders and mental patients in society.

Keywords: mental health, schools, mental illness, conception

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Authors: Saradha Priya

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Developing virtual human is very important to meet the challenges occurred in many applications where human find difficult or risky to perform the task. A robot is a machine that can perform a task automatically or with guidance. Robotics is generally a combination of artificial intelligence and physical machines (motors). Computational intelligence involves the programmed instructions. This project proposes a robotic vehicle that has a camera, PIR sensor and text command based movement. It is specially designed to perform surveillance and other few tasks in the most efficient way. Serial communication has been occurred between a remote Base Station, GUI Application, and PC.

Keywords: Zigbee, camera, pirsensor, wireless transmission, DC motor

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Authors: Martin Goubej, Tomas Popule, Alois Krejci

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This paper deals with experimental identification of mechanical systems with nonlinear friction characteristics. Dynamic LuGre friction model is adopted and a systematic approach to parameter identification of both linear and nonlinear subsystems is given. The identification procedure consists of three subsequent experiments which deal with the individual parts of plant dynamics. The proposed method is experimentally verified on an industrial-grade robotic manipulator. Model fidelity is compared with the results achieved with a static friction model.

Keywords: mechanical friction, LuGre model, friction identification, motion control

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Authors: Fen-Ling Kuo, Hsin-Chieh Lee, Han-Yun Hsiao, Jui-Chi Lin

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Background: Children with cerebral palsy (CP) usually suffered from mild to maximum upper limb dysfunction such as having difficulty in reaching and picking up objects, which profoundly affects their participation in activities of daily living (ADLs). Robot-assisted rehabilitation provides intensive physical training in improving sensorimotor function of the hand. Many researchers have extensively studied the effects of robot-assisted therapy (RT) for the paretic upper limb in patients with stroke in recent years. However, few studies have examined the effect of RT on hand function in children with CP. The purpose of this study is to investigate the effectiveness of Gloreha Sinfonia, a robotic device with a dynamic arm support system mainly focus on distal upper-limb training, on improvements of hand function and ADLs in children with CP. Methods: Seven children with moderate CP were recruited in this case series study. RT using Gloreha Sinfonia was performed 2 sessions per week, 60 min per session for 6 consecutive weeks, with 12 times in total. Outcome measures included the Fugl-Meyer Assessment-upper extremity (FMA-UE), the Box and Block Test, the electromyography activity of the extensor digitorum communis muscle (EDC) and brachioradialis (BR), a grip dynamometer for motor evaluation, and the ABILHAND-Kids for measuring manual ability to manage daily activities, were performed at baseline, after 12 sessions (end of treatment) and at the 1-month follow-up. Results: After 6 weeks of robot-assisted treatment of hand function, there were significant increases in FMA-UE shoulder/elbow scores (p=0.002), FMA-UE wrist/hand scores (p=0.002), and FMA-UE total scores (p=0.002). There were also significant improvements in the BR mean value (p = 0.015) and electrical agonist-antagonist muscle ratio (p=0.041) in grasping a 1-inch cube task. These gains were maintained for a month after the end of the intervention. Conclusion: RT using Gloreha Sinfonia for hand function training may contribute toward the improvement of upper extremity function and efficacy in recruiting BR muscle in children with CP. The results were maintained at one month after intervention.

Keywords: activities of daily living, cerebral palsy, hand function, robotic rehabilitation

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Authors: Babar Khan, Wang Zhijie

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Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric.

Keywords: automatic classification, texture descriptor, colour descriptor, opponent colour channel

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Authors: Yifan Lu, Ying Cui, Ying Zhu, Edward S. Lu, Rebecca Zeng, Rohan Bajaj, Raviv Katz, Rongrong Le, Jay C. Wang, John B. Miller

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Purpose: Retinal artery occlusion (RAO) is an ophthalmic emergency that can lead to poor visual outcome and is associated with an increased risk of cerebral stroke and cardiovascular events. Fluorescein angiography (FA) is the traditional diagnostic tool for RAO; however, wide-field swept-source optical coherence tomography angiography (WF SS-OCTA), as a nascent imaging technology, is able to provide quick and non-invasive angiographic information with a wide field of view. In this study, we looked for associations between OCT-A vascular metrics and visual acuity in patients with prior diagnosis of RAO. Methods: Patients with diagnoses of central retinal artery occlusion (CRAO) or branched retinal artery occlusion (BRAO) were included. A 6mm x 6mm Angio and a 15mm x 15mm AngioPlex Montage OCT-A image were obtained for both eyes in each patient using the Zeiss Plex Elite 9000 WF SS-OCTA device. Each 6mm x 6mm image was divided into nine Early Treatment Diabetic Retinopathy Study (ETDRS) subfields. The average measurement of the central foveal subfield, inner ring, and outer ring was calculated for each parameter. Non-perfusion area (NPA) was manually measured using 15mm x 15mm Montage images. A linear regression model was utilized to identify a correlation between the imaging metrics and visual acuity. A P-value less than 0.05 was considered to be statistically significant. Results: Twenty-five subjects were included in the study. For RAO eyes, there was a statistically significant negative correlation between vision and retinal thickness as well as superficial capillary plexus vessel density (SCP VD). A negative correlation was found between vision and deep capillary plexus vessel density (DCP VD) without statistical significance. There was a positive correlation between vision and choroidal thickness as well as choroidal volume without statistical significance. No statistically significant correlation was found between vision and the above metrics in contralateral eyes. For NPA measurements, no significant correlation was found between vision and NPA. Conclusions: This is the first study to our best knowledge to investigate the utility of WF SS-OCTA in RAO and to demonstrate correlations between various retinal vascular imaging metrics and visual outcomes. Further investigations should explore the associations between these imaging findings and cardiovascular risk as RAO patients are at elevated risk for symptomatic stroke. The results of this study provide a basis to understand the structural changes involved in visual outcomes in RAO. Furthermore, they may help guide management of RAO and prevention of cerebral stroke and cardiovascular accidents in patients with RAO.

Keywords: OCTA, swept-source OCT, retinal artery occlusion, Zeiss Plex Elite

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Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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Authors: Tamara Grujic, Mirjana Bonkovic

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Measurements and quantitative analysis of kinematic parameters of human hand movements have an important role in different areas such as hand function rehabilitation, modeling of multi-digits robotic hands, and the development of machine-man interfaces. In this paper the assessment and evaluation of the reach-to-grasp movement by using computerized and robot-assisted method is described. Experiment involved the measurements of hand positions of seven healthy subjects during grasping three objects of different shapes and sizes. Results showed that three dominant phases of reach-to-grasp movements could be clearly identified.

Keywords: human hand, kinematics, measurement and analysis, reach-to-grasp movement

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Authors: Vinicius Binotte, Guilherme Baldo, Christiane Goulart, Carlos Valadão, Eliete Caldeira, Teodiano Bastos

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Socially assistive robotic has become increasingly active and it is present in therapies of people affected for several neurobehavioral conditions, such as Autism Spectrum Disorder (ASD). In fact, robots have played a significant role for positive interaction with children with ASD, by stimulating their social and cognitive skills. This work introduces a mobile socially-assistive robot, which was built for interaction with children with ASD, using non-linear control techniques for this interaction.

Keywords: socially assistive robotics, mobile robot, autonomous control, autism

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Authors: Jatin Gupta, Bishakh Bhattacharya

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With the aging process, many people start suffering from the problem of weak limbs resulting in mobility disorders and loss of sensory and motor function of limbs. Wearable robotic devices are viable solutions to help people suffering from these issues by augmenting their strength. These robotic devices, popularly known as exoskeletons aides user by providing external power and controlling the dynamics so as to achieve desired motion. Present work studies a simplified dynamic model of the human gait. A four link open chain kinematic model is developed to describe the dynamics of Single Support Phase (SSP) of the human gait cycle. The dynamic model is developed integrating mathematical models of the motion of inverted and triple pendulums. Stance leg is modeled as inverted pendulum having single degree of freedom and swing leg as triple pendulum having three degrees of freedom viz. thigh, knee, and ankle joints. The kinematic model is formulated using forward kinematics approach. Lagrangian approach is used to formulate governing dynamic equation of the model. For a system of nonlinear differential equations, numerical method is employed to obtain system response. Reference trajectory is generated using human body simulator, LifeMOD. For optimal mechanical design and controller design of exoskeleton system, it is imperative to study parameter sensitivity of the system. Six different parameters viz. thigh, shank, and foot masses and lengths are varied from 85% to 115% of the original value for the present work. It is observed that hip joint of swing leg is the most sensitive and ankle joint of swing leg is the least sensitive one. Changing link lengths causes more deviation in system response than link masses. Also, shank length and thigh mass are most sensitive parameters. Finally, the present study gives an insight on different factors that should be considered while designing a lower extremity exoskeleton.

Keywords: lower limb exoskeleton, multibody dynamics, energy based formulation, optimal design

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Authors: Altab Hossain, Shakerul Islam, Golamur R. Khan, Abu Zafar M. Salahuddin

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An effective identification of breakdowns is of premier importance for the safe and reliable operation of Nuclear Power Plants (NPP) and its associated facilities. A great number of monitoring and diagnosis methodologies are applied and used worldwide in areas such as industry, automobiles, hospitals, and power plant to detect and reduce human disasters. The potential consequences of several hazardous activities may harm the society using nuclear and its associated facilities. Hence, one of the most popular and effective methods to ensure safety and monitor the entire nuclear facility and imply risk-free operation without human interference during the hazardous situation is using a robot. Therefore, in this study, an advanced autonomous robot has been designed and developed that can monitor several parameters in the NPP to ensure the safety and do some risky job in case of nuclear disaster. The robot consisted of autonomous track following unit, data processing and transmitting unit can follow a straight line and take turn as the bank greater than 90 degrees. The developed robot can analyze various parameters such as temperature, altitude, radiation, obstacle, humidity, detecting fire, measuring distance, ultrasonic scan and taking the heat of any particular object. It has an ability to broadcast live stream and can record the document to its own server memory. There is a separate control unit constructed with a baseboard which processes the recorded data and a transmitter which transmits the processed data. To make the robot user-friendly, the code is developed such a way that a user can control any of robotic arm as per types of work. To control at any place and without the track, there is an advanced code has been developed to take manual overwrite. Through this process, administrator who has logged in permission to Dynamic Host Client Protocol (DHCP) can make the handover of the control of the robot. In this process, this robot is provided maximum nuclear security from being hacked. Not only NPP, this robot can be used to maximize the real-time monitoring system of any nuclear facility as well as nuclear material transportation and decomposition system.

Keywords: nuclear power plant, radiation, dynamic host client protocol, nuclear security

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Authors: Suehyun Cho, Darrell Harrington, Florent Cros, Olin Palmer, John Caputo, Michael Kardosh, Eran Oren, William Loudon, Alex Kiselyov, Michael Shpigelmacher

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Bionaut Labs, LLC is developing a minimally invasive robotic microdevice designed to treat non-communicating hydrocephalus in both adult and pediatric patients. The device utilizes biocompatible microsurgical particles (Bionaut™) that are specifically designed to safely and reliably perform accurate fenestration(s) in the 3rd ventricle, aqueduct of Sylvius, and/or trapped intraventricular cysts of the brain in order to re-establish normal cerebrospinal fluid flow dynamics and thereby balance and/or normalize intra/intercompartmental pressure. The Bionaut™ is navigated to the target via CSF or brain tissue in a minimally invasive fashion with precise control using real-time imaging. Upon reaching the pre-defined anatomical target, the external driver allows for directing the specific microsurgical action defined to achieve the surgical goal. Notable features of the proposed protocol are i) Bionaut™ access to the intraventricular target follows a clinically validated endoscopy trajectory which may not be feasible via ‘traditional’ rigid endoscopy: ii) the treatment is microsurgical, there are no foreign materials left behind post-procedure; iii) Bionaut™ is an untethered device that is navigated through the subarachnoid and intraventricular compartments of the brain, following pre-designated non-linear trajectories as determined by the safest anatomical and physiological path; iv) Overall protocol involves minimally invasive delivery and post-operational retrieval of the surgical Bionaut™. The approach is expected to be suitable to treat pediatric patients 0-12 months old as well as adult patients with obstructive hydrocephalus who fail traditional shunts or are eligible for endoscopy. Current progress, including platform optimization, Bionaut™ control, and real-time imaging and in vivo safety studies of the Bionauts™ in large animals, specifically the spine and the brain of ovine models, will be discussed.

Keywords: Bionaut™, cerebrospinal fluid, CSF, fenestration, hydrocephalus, micro-robot, microsurgery

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Authors: Simiao Ren, En Wei

Abstract:

Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

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Authors: Kashika P. H.

Abstract:

The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

Procedia PDF Downloads 78

Authors: Johanna Perdomo, Riki Lamont, Edmund Pickering, Naomi C. Paxton, Maria A. Woodruff

Abstract:

Background: Tissue Engineering (TE) has combined advanced materials, such as biomaterials, to create affordable scaffolds and dynamic systems to generate stimulation of seeded cells on these scaffolds, improving and maintaining the cellular growth process in a cell culture. However, Few TE skin products have been clinically translated, and more research is required to produce highly biomimetic skin substitutes that mimic the native elasticity of skin in a controlled manner. Therefore, this work will be focused on the fabrication of a novel mechanical system to enhance the TE treatment approaches for the reparation of damaged tissue skin. Aims: To archive this, a soft robotic device will be created to emulate different deformation of skin stress. The design of this soft robot will allow the attachment of scaffolds, which will then be mechanically actuated. This will provide a novel and highly adaptable platform for dynamic cell culture. Methods: Novel, low-cost soft robot is fabricated via 3D printed moulds and silicone. A low cost, electro-mechanical device was constructed to actuate the soft robot through the controlled combination of positive and negative air pressure to control the different state of movements. Mechanical tests were conducted to assess the performance and calibration of each electronic component. Similarly, pressure-displacement test was performed on scaffolds, which were attached to the soft robot, applying various mechanical loading regimes. Lastly, digital image correlation test was performed to obtain strain distributions over the soft robot’s surface. Results: The control system can control and stabilise positive pressure changes for long hours. Similarly, pressure-displacement test demonstrated that scaffolds with 5µm of diameter and wavy geometry can displace at 100%, applying a maximum pressure of 1.5 PSI. Lastly, during the inflation state, the displacement of silicone was measured using DIC method, and this showed a parameter of 4.78 mm and strain of 0.0652. Discussion And Conclusion: The developed soft robot system provides a novel and low-cost platform for the dynamic actuation of tissue scaffolds with a target towards dynamic cell culture.

Keywords: soft robot, tissue engineering, mechanical stimulation, dynamic cell culture, bioreactor

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Authors: J. G. Batista, K. N. Sousa, ¬J. L. Nunes, R. L. S. Sousa, G. A. P. Thé

Abstract:

This work deals with parameter identification of permanent magnet motors, a class of ac motor which is particularly important in industrial automation due to characteristics like applications high performance, are very attractive for applications with limited space and reducing the need to eliminate because they have reduced size and volume and can operate in a wide speed range, without independent ventilation. By using experimental data and genetic algorithm we have been able to extract values for both the motor inductance and the electromechanical coupling constant, which are then compared to measured and/or expected values.

Keywords: modeling, AC servomotor, permanent magnet synchronous motor-PMSM, genetic algorithm, vector control, robotic manipulator, control

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

Abstract:

Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

Procedia PDF Downloads 205

Authors: Annika Schmidt, Ausama Hadi Ahmed, Carlo Menon

Abstract:

End effectors for robotic systems are becoming more and more advanced, resulting in a growing variety of gripping tasks. However, most grippers are application specific. This paper presents a gripper that interacts with an object’s surface rather than being dependent on a defined shape or size. For this purpose, ingressive and astrictive features are combined to achieve the desired gripping capabilities. The developed prototype is tested on a variety of surfaces with different hardness and roughness properties. The results show that the gripping mechanism works on all of the tested surfaces. The influence of the material properties on the amount of the supported load is also studied and the efficiency is discussed.

Keywords: claw, dry adhesion, insects, material properties

Procedia PDF Downloads 331