A Vision-Based Approach for Building Telecare and Telerehabilitation Services

Barriga, Angela; Conejero, José María; Hernández, Juan; Jurado, Elena; Moguel, Enrique

doi:10.3390/s16101724

Cited by 26 publications

(19 citation statements)

References 31 publications

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“…A depth camera/sensor [20,21] is independent of illumination and many fall-detection methods have benefited from depth-based algorithms [22]. Previous studies [23,24,25,26] used the 3D coordinates of the skeleton as features to successfully detect falls. However, these methods heavily depend on the skeleton-detection results from Kinect SDK or OpenNI, which are not reliable [8,22].…”

Section: Introductionmentioning

confidence: 99%

Robust Self-Adaptation Fall-Detection System Based on Camera Height

Kong

Chen

Wang

et al. 2019

Sensors

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Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

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Section: Introductionmentioning

confidence: 99%

Robust Self-Adaptation Fall-Detection System Based on Camera Height

Kong

Chen

Wang

et al. 2019

Sensors

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“…It is considered as a partial replacement of face-to-face physical rehabilitation [9]. Tele-rehabilitation uses mainly two groups of technologies: (1) wearable devices and (2) vision-based systems based on depth cameras and intelligent algorithms [10]. In [5], the authors describe and analyze some characteristics and typical requirements tele-rehabilitation systems.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Usability and Accessibility in Tele-Rehabilitation Systems

Pérez-Medina¹,

Acosta-Vargas²,

Rybarczyk³

2019

Assistive and Rehabilitation Engineering

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The appropriate development of tele-rehabilitation platforms requires the involvement and iterative assessments of potential users and experts in usability. Usability consists of measuring the degree to which an interactive system can be used by specified final users to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use. Usability studies need to be complemented by an accessibility assessment. Accessibility indicates how easy it is for a person to access any content, regardless of their physical, educational, social, psychological, or cultural conditions. This chapter intends to conduct a systematic review of the literature on usability and accessibility in tele-rehabilitation platforms carried out through the PRISMA method. To do so, we searched in ACM, IEEE Xplore, Google Scholar, and Scopus databases for the most relevant papers of the last decade. The main result of the usability shows that the user experience predominates over the heuristic studies, and the usability questionnaire most used in user experience is the SUS. The main result of the accessibility reveals that the topic is only marginally studied. In addition, it is observed that Web applications do not apply the physical and cognitive accessibility standards defined by the WCAG 2.1.

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“…The user displacement challenge to address to the rehabilitation center has been overcome due to the advances in computer network technology, a new technique has brought the possibility of delivering rehabilitation services to users far from the rehabilitation centers. This technique -known as telerehabilitation [23] has among others, the goal of extending and improving user care [23], [24].…”

mentioning

confidence: 99%

The Augmented Reality Telerehabilitation System for Powered Wheelchair User’s Training

Caetano

Valentini²,

Mattioli

et al. 2020

JCIS

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Many people worldwide have been experimenting a decrease in their mobility as a result of aging, accidents and degenerative diseases. In many cases, a Powered Wheelchair (PW) is an alternative help. Currently in Brazil, patients can receive a PW from the Unified Health System, following prescription criteria. However, they do not have an appropriate previous training for driving the PW. Consequently, users might suffer accidents since a customized training protocol is not available. Nevertheless, due to financial and/or health limitations, many users are unable to attend a rehabilitation center. To overcome these limitations, we developed an Augmented Reality Telerehabilitation System Architecture based on the Power Mobility Road Test (PMRT) for supporting PW user's training. In this system, the therapists can remotely customize and evaluate training tasks and the user can perform the training in safer conditions. The video stream and data transfer between each environment were made possible through UDP (User Datagram Protocol). To evaluate and present the system architecture potential, a preliminary test was conducted with 3 spinal cord injury participants. They performed 3 basic training protocols defined by a therapist. The following metrics were adopted for evaluation: number of control commands; elapsed time; number of collisions; biosignals. Also, a questionary was used to evaluate system features. Preliminary results demonstrated the specific needs of individuals using a PW thanks to adopted metrics (qualitative and emotional). Results have shown the need for a training system with customizable protocols to fulfill these needs. User's evaluation demonstrates that the combination of AR techniques with PMRT adaptations, increases user's well-being after training sessions. Furthermore, a training experience helps users to overcome their displacement problems, as well as for appointing challenges before large scale use. This system architecture allows further studies on telerehabilitation of PW users training.

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A Vision-Based Approach for Building Telecare and Telerehabilitation Services

Cited by 26 publications

References 31 publications

Robust Self-Adaptation Fall-Detection System Based on Camera Height

Robust Self-Adaptation Fall-Detection System Based on Camera Height

A Systematic Review of Usability and Accessibility in Tele-Rehabilitation Systems

The Augmented Reality Telerehabilitation System for Powered Wheelchair User’s Training

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