A Review of Techniques on Gait-Based Person Re-Identification

Copyright (c) 2023 Babak Rahi, Maozhen Li and Man Qi. Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.This research received no external funding

Context-aware gestural interaction in the smart environments of the ubiquitous computing era

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyTechnology is becoming pervasive and the current interfaces are not adequate for the interaction with the smart environments of the ubiquitous computing era. Recently, researchers have started to address this issue introducing the concept of natural user interface, which is mainly based on gestural interactions. Many issues are still open in this emerging domain and, in particular, there is a lack of common guidelines for coherent implementation of gestural interfaces. This research investigates gestural interactions between humans and smart environments. It proposes a novel framework for the high-level organization of the context information. The framework is conceived to provide the support for a novel approach using functional gestures to reduce the gesture ambiguity and the number of gestures in taxonomies and improve the usability. In order to validate this framework, a proof-of-concept has been developed. A prototype has been developed by implementing a novel method for the view-invariant recognition of deictic and dynamic gestures. Tests have been conducted to assess the gesture recognition accuracy and the usability of the interfaces developed following the proposed framework. The results show that the method provides optimal gesture recognition from very different view-points whilst the usability tests have yielded high scores. Further investigation on the context information has been performed tackling the problem of user status. It is intended as human activity and a technique based on an innovative application of electromyography is proposed. The tests show that the proposed technique has achieved good activity recognition accuracy. The context is treated also as system status. In ubiquitous computing, the system can adopt different paradigms: wearable, environmental and pervasive. A novel paradigm, called synergistic paradigm, is presented combining the advantages of the wearable and environmental paradigms. Moreover, it augments the interaction possibilities of the user and ensures better gesture recognition accuracy than with the other paradigms

Hardware for recognition of human activities: a review of smart home and AAL related technologies

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard

Walking with virtual humans : understanding human response to virtual humanoids' appearance and behaviour while navigating in immersive VR

In this thesis, we present a set of studies whose results have allowed us to analyze how to improve the realism, navigation, and behaviour of the avatars in an immersive virtual reality environment. In our simulations, participants must perform a series of tasks and we have analyzed perceptual and behavioural data. The results of the studies have allowed us to deduce what improvements are needed to be incorporated to the original simulations, in order to enhance the perception of realism, the navigation technique, the rendering of the avatars, their behaviour or their animations. The most reliable technique for simulating avatars’ behaviour in a virtual reality environment should be based on the study of how humans behave within the environment. For this purpose, it is necessary to build virtual environments where participants can navigate safely and comfortably with a proper metaphor and, if the environment is populated with avatars, simulate their behaviour accurately. All these aspects together will make the participants behave in a way that is closer to how they would behave in the real world. Besides, the integration of these concepts could provide an ideal platform to develop different types of applications with and without collaborative virtual reality such as emergency simulations, teaching, architecture, or designing. In the first contribution of this thesis, we carried out an experiment to study human decision making during an evacuation. We were interested to evaluate to what extent the behaviour of a virtual crowd can affect individuals' decisions. From the second contribution, in which we studied the perception of realism with bots and humans performing just locomotion or varied animations, we can conclude that the combination of having human-like avatars with animation variety can increase the overall realism of a crowd simulation, trajectories and animation. The preliminary study presented in the third contribution of this thesis showed that realistic rendering of the environment and the avatars do not appear to increase the perception of realism in the participants, which is consistent with works presented previously. The preliminary results in our walk-in-place contribution showed a seamless and natural transition between walk-in-place and normal walk. Our system provided a velocity mapping function that closely resembles natural walk. We observed through a pilot study that the system successfully reduces motion sickness and enhances immersion. Finally, the results of the contribution related to locomotion in collaborative virtual reality showed that animation synchronism and footstep sound of the avatars representing the participants do not seem to have a strong impact in terms of presence and feeling of avatar control. However, in our experiment, incorporating natural animations and footstep sound resulted in smaller clearance values in VR than previous work in the literature. The main objective of this thesis was to improve different factors related to virtual reality experiences to make the participants feel more comfortable in the virtual environment. These factors include the behaviour and appearance of the virtual avatars and the navigation through the simulated space in the experience. By increasing the realism of the avatars and facilitating navigation, high scores in presence are achieved during the simulations. This provides an ideal framework for developing collaborative virtual reality applications or emergency simulations that require participants to feel as if they were in real life.En aquesta tesi, es presenta un conjunt d'estudis els resultats dels quals ens han permès analitzar com millorar el realisme, la navegació i el comportament dels avatars en un entorn de realitat virtual immersiu. En les nostres simulacions, els participants han de realitzar una sèrie de tasques i hem analitzat dades perceptives i de comportament mentre les feien. Els resultats dels estudis ens han permès deduir quines millores són necessàries per a ser incorporades a les simulacions originals, amb la finalitat de millorar la percepció del realisme, la tècnica de navegació, la representació dels avatars, el seu comportament o les seves animacions. La tècnica més fiable per simular el comportament dels avatars en un entorn de realitat virtual hauria de basar-se en l'estudi de com es comporten els humans dins de l¿entorn virtual. Per a aquest propòsit, és necessari construir entorns virtuals on els participants poden navegar amb seguretat i comoditat amb una metàfora adequada i, si l¿entorn està poblat amb avatars, simular el seu comportament amb precisió. Tots aquests aspectes junts fan que els participants es comportin d'una manera més pròxima a com es comportarien en el món real. A més, la integració d'aquests conceptes podria proporcionar una plataforma ideal per desenvolupar diferents tipus d'aplicacions amb i sense realitat virtual col·laborativa com simulacions d'emergència, ensenyament, arquitectura o disseny. En la primera contribució d'aquesta tesi, vam realitzar un experiment per estudiar la presa de decisions durant una evacuació. Estàvem interessats a avaluar en quina mesura el comportament d'una multitud virtual pot afectar les decisions dels participants. A partir de la segona contribució, en la qual estudiem la percepció del realisme amb robots i humans que realitzen només una animació de caminar o bé realitzen diverses animacions, vam arribar a la conclusió que la combinació de tenir avatars semblants als humans amb animacions variades pot augmentar la percepció del realisme general de la simulació de la multitud, les seves trajectòries i animacions. L'estudi preliminar presentat en la tercera contribució d'aquesta tesi va demostrar que la representació realista de l¿entorn i dels avatars no semblen augmentar la percepció del realisme en els participants, que és coherent amb treballs presentats anteriorment. Els resultats preliminars de la nostra contribució de walk-in-place van mostrar una transició suau i natural entre les metàfores de walk-in-place i caminar normal. El nostre sistema va proporcionar una funció de mapatge de velocitat que s'assembla molt al caminar natural. Hem observat a través d'un estudi pilot que el sistema redueix amb èxit el motion sickness i millora la immersió. Finalment, els resultats de la contribució relacionada amb locomoció en realitat virtual col·laborativa van mostrar que el sincronisme de l'animació i el so dels avatars que representen els participants no semblen tenir un fort impacte en termes de presència i sensació de control de l'avatar. No obstant això, en el nostre experiment, la incorporació d'animacions naturals i el so de passos va donar lloc a valors de clearance més petits en RV que treballs anteriors ja publicats. L'objectiu principal d'aquesta tesi ha estat millorar els diferents factors relacionats amb experiències de realitat virtual immersiva per fer que els participants se sentin més còmodes en l'entorn virtual. Aquests factors inclouen el comportament i l'aparença dels avatars i la navegació a través de l'entorn virtual. En augmentar el realisme dels avatars i facilitar la navegació, s'aconsegueixen altes puntuacions en presència durant les simulacions. Això proporciona un marc ideal per desenvolupar aplicacions col·laboratives de realitat virtual o simulacions d'emergència que requereixen que els participants se sentin com si estiguessin en la vida realPostprint (published version

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

VRShape: A Virtual Reality Tool for Shaping Movement Compensation

The majority of persons living with chronic stroke experience some form of upper extremity motor impairment that affects their functional movement, performance of meaningful activities, and participation in the flow of daily life. Stroke survivors often compensate for these impairments by adapting their movement patterns to incorporate additional degrees of freedom at new joints and body segments. One of the most common compensatory movements is the recruitment of excessive trunk flexion when reaching with the affected upper extremity. Long-term use of these compensations may lead to suboptimal motor recovery and chronic pain or injury due to overuse. Rehabilitation focuses on repetitive practice with the impaired limb to stimulate motor learning and neuroplasticity; however, few interventions achieve the required repetition dose or address the possible negative effects of compensatory movements. Virtual reality (VR) is an emerging tool in rehabilitation science that may be capable of (1) objectively measuring compensation during upper extremity movement, (2) motivating persons to perform large doses of repetitive practice through the integration of virtual environments and computer games, and (3) providing the basis for a motor intervention aimed at improving motor performance and incrementally reducing, or shaping, compensation. The purpose of this project was to develop and test a VR tool with these capabilities for shaping movement compensation for persons with chronic stroke, and to achieve this we performed three separate investigations (Chapters 2-4).First, we investigated the validity and reliability of two generations of an off-the-shelf motion sensor, namely the Microsoft Kinect, for measuring trunk compensations during reaching (Chapter 2). A small group of healthy participants performed various reaching movements on two separate days while simultaneously being recorded by the two sensors and a third considered to be the gold standard. We found that the second generation Kinect sensor was more accurate and showed greater validity for measuring trunk flexion relative to the gold standard, especially during extended movements, and therefore recommended that sensor for future VR development. Research with a more heterogeneous and representative population, such as persons with stroke, will further improve the evaluation of these sensors in future work.Second, we tested a newly-designed VR tool, VRShape, for use during a single session of upper extremity movement practice (Chapter 3). VRShape integrates the Microsoft Kinect and custom software to convert upper extremity movements into the control of various virtual environments and computer games while providing real-time feedback about compensation. A small group of participants with stroke used VRShape to repetitively perform reaching movements while simultaneously receiving feedback concerning their trunk flexion relative to a calibrated threshold. Our tool was able to elicit a large number of successful reaches and limit the amount of trunk flexion used during a single practice session while remaining usable, motivating, and safe. However, areas of improvement were identified relative to the efficiency of the software and the variety of virtual environments available. Third, we implemented VRShape over the course of a motor intervention for persons with stroke and evaluated its feasibility and effect on compensation during reaching tasks (Chapter 4). A small group of participants took part in 18 interventions session using VRShape for repetitive reaching practice with incrementally shaped trunk compensation. Trunk flexion decreased significantly and reaching kinematics improved significantly as a result of the intervention. Even with extended use, participants were able to complete intense practice and thousands of repetitions while continually rating the system as usable, motivating, engaging, and safe. Our VR tool demonstrated feasibility and preliminary efficacy within a small study, but future work is needed to identify its ideal applications and address its limitations. In summary, this project shows that use of a VR tool incorporating an accurate sensor (Chapter 2) and feedback from initial testing (Chapter 3) is capable of changing the amount of trunk flexion used during reaching movements for persons with stroke (Chapter 4). More research is needed to establish its efficacy and effectiveness, but improvements in motor recovery and associated decreases in compensation associated with the use of VRShape are important rehabilitation goals that may lead to improved participation and quality of life for persons living with long-term impairments due to chronic stroke

Proceedings of the 10th international conference on disability, virtual reality and associated technologies (ICDVRAT 2014)

The proceedings of the conferenc

A Scoping Review on Virtual Reality-Based Industrial Training

The fourth industrial revolution has forced most companies to technologically evolve, applying new digital tools, so that their workers can have the necessary skills to face changing work environments. This article presents a scoping review of the literature on virtual reality-based training systems. The methodology consisted of four steps, which pose research questions, document search, paper selection, and data extraction. From a total of 350 peer-reviewed database articles, such as SpringerLink, IEEEXplore, MDPI, Scopus, and ACM, 44 were eventually chosen, mostly using the virtual reality haptic glasses and controls from Oculus Rift and HTC VIVE. It was concluded that, among the advantages of using this digital tool in the industry, is the commitment, speed, measurability, preservation of the integrity of the workers, customization, and cost reduction. Even though several research gaps were found, virtual reality is presented as a present and future alternative for the efficient training of human resources in the industrial field.This work was supported by Instituto Superior Tecnológico Victoria Vásconez Cuvi. The authors appreciate the opportunity to analyze topics related to this paper. The authors must also recognize the supported bringing by Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

The majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants