Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods

KinFit: A factual aerobic sport game with stimulation support

Overweight and obesity is a situation where a person has stacked too much fat that might affect negatively his/her health. Many people skip doing exercises due to several facts related to the encouragement, health-awareness, and time arrangement. Diverse aerobic video games have been proposed to help users in doing exercises. However, we observe some limitations in existing games. For instance, they don't give correct scores while wearing Arabic traditional suits, they don't consider showing immersive realistic scenes, and they don't stimulate users to do exercises and keeping them encouraged to play more. We propose in this paper an aerobic video game that displays real scenes of aerobic coaches and keeps the user notified about doing exercises. It is a kind of serious games that allows users to learn aerobic movements and practice with aerobic coaches. It contains several exercises in which each can be played on normal screen or in fully immersive virtual reality (VR). While the user is playing, he/she can see the playing score with the estimated amount of burned calories. It stores the time when the user plays to remind him/her about doing exercises again. The profound user studies demonstrated the usability and effectiveness of the proposed game. 2018 Kassel University Press GmbH.The authors would like to acknowledge that devices and equipment were provided by the Visual Computing Research Center, Department of Computer Science and Engineering, at Qatar University. This publication was supported by Qatar University Collaborative High Impact Grant QUHI-CENG-18/19-1. The content of this article and its quality are solely the responsibility of the authors and do not necessarily represent the official views of Qatar University.Scopu

Coaching or gaming? Implications of strategy choice for home based stroke rehabilitation

Background: The enduring aging of the world population and prospective increase of age-related chronic diseases urge the implementation of new models for healthcare delivery. One strategy relies on ICT (Information and Communications Technology) home-based solutions allowing clients to pursue their treatments without institutionalization. Stroke survivors are a particular population that could strongly benefit from such solutions, but is not yet clear what the best approach is for bringing forth an adequate and sustainable usage of home-based rehabilitation systems. Here we explore two possible approaches: coaching and gaming. Methods: We performed trials with 20 healthy participants and 5 chronic stroke survivors to study and compare execution of an elbow flexion and extension task when performed within a coaching mode that provides encouragement or within a gaming mode. For each mode we analyzed compliance, arm movement kinematics and task scores. In addition, we assessed the usability and acceptance of the proposed modes through a customized self-report questionnaire. Results: In the healthy participants sample, 13/20 preferred the gaming mode and rated it as being significantly more fun (p < .05), but the feedback delivered by the coaching mode was subjectively perceived as being more useful (p < .01). In addition, the activity level (number of repetitions and total movement of the end effector) was significantly higher (p <.001) during coaching. However, the quality of movements was superior in gaming with a trend towards shorter movement duration (p=.074), significantly shorter travel distance (p <.001), higher movement efficiency (p <.001) and higher performance scores (p <.001). Stroke survivors also showed a trend towards higher activity levels in coaching, but with more movement quality during gaming. Finally, both training modes showed overall high acceptance. Conclusions: Gaming led to higher enjoyment and increased quality in movement execution in healthy participants. However, we observed that game mechanics strongly determined user behavior and limited activity levels. In contrast, coaching generated higher activity levels. Hence, the purpose of treatment and profile of end-users has to be considered when deciding on the most adequate approach for home based stroke rehabilitation

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Telerobotic 3D Articulated Arm-Assisted Surgery Tools with Augmented Reality for Surgery Training

In this research, human body will be marked and tracked using depth camera. The arm motion from the trainer will be sent through network and then mapped into 3D robotic arm in the destination server. The robotic arm will move according to the trainer. In the meantime, trainee will follow the movement and they can learn how to do particular tasks according to the trainer. The telerobotic-assisted surgery tools will give guidance how to slice or do simple surgery in several steps through the 3D medical images which are displayed in the human body. User will do training and selects some of the body parts and then analyzes it. The system provide specific task to be completed during training and measure how many tasks the user can accomplish during the surgical time. The telerobotic-assisted virtual surgery tools using augmented reality (AR) is expected to be used widely in medical education as an alternative system with low-cost solution

Towards a virtual coach for boccia: developing a virtual augmented interaction based on a boccia simulator

Science and Technology Publications, Lda. All rights reserved. Disability can be a factor that leads to social exclusion. Considering that involvement in society is paramount for a person with disability, participation in sports can be a powerful tool for inclusion. Based on this premise, the authors propose an intelligent virtual coach for Boccia to encourage the practice of this sport on persons with disabilities, while promoting social inclusion and shortening the learning curve for individuals new to the sport by learning about game strategy. The envisioned virtual coach will rely on Artificial Intelligence models, thus requiring the creation of large datasets, namely for ball placement and throwing movement recommendations. To answer these problems, this work is focused on the development of a Boccia simulator. With this simulator, it is possible to generate artificial gameplay images and allow the user to control an avatar with body tracking. Gesture recognition was implemented with a state-machine, thus enabling the player to throw the ball, with customizable physics, by performing one of two different throwing movements. This functionality can allow the recording of data describing the body movement associated with the placement of the ball in a certain position within the virtual court, which is essential for the proposed recommendation system.FCT - Fundação para a Ciência e a Tecnologia(690874).This article is supported by the project Deus ex Machina: NORTE – 01 – 0145 – FEDER - 000026, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. Vinicius Silva also thanks FCT for the PhD scholarship SFRH/BD/133314/2017