eFisioTrack: A Telerehabilitation Environment Based on Motion Recognition Using Accelerometry

Ruiz-Fernández, Daniel; Marín-Alonso, Oscar; Soriano-Payá, Antonio; García-Pérez, Joaquín D.

doi:10.1155/2014/495391

Cited by 11 publications

(9 citation statements)

References 29 publications

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“…Telerehabilitation can help with this issue by discharging patients from points of care while improving their adherence to treatment [6]. Recent telerehabilitation studies have addressed different approaches: feasibility and efficacy, patients’ and professionals’ satisfaction, and cost analysis studies.…”

Section: Introductionmentioning

confidence: 99%

Evidence of Benefit of Telerehabitation After Orthopedic Surgery: A Systematic Review

et al. 2017

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BackgroundIn addition to traditional physiotherapy, studies based on telerehabilitation programs have published the results of effectiveness, validity, noninferiority, and important advantages in some neurological, cognitive, and musculoskeletal disorders, providing an opportunity to define new social policies and interventions.ObjectivesThe aim of this systematic review is to investigate the effects of telerehabilitation after surgical procedures on orthopedic conditions as well as to describe how interventions are designed and to determine whether telerehabilitation is comparable with conventional methods of delivery. This systematic review summarizes the levels of evidence and grades of recommendation regarding telerehabilitation intervention (synchronous or asynchronous provided via the telerehabilitation medium, either in conjunction with, or in isolation of, other treatment interventions) after surgical procedures on orthopedic conditions.MethodsStudy quality was assessed using the Physiotherapy Evidence Database (PEDro) scores and grade of recommendation following the recommendation of the Oxford Centre for Evidence-Based Medicine.ResultsWe found 3 studies with PEDro scores between 6 and 8, which is considered as level 1 evidence (good; 20% [3/15]), 4 studies with a score of 5, which is considered as level 2 evidence (acceptable; 27% [4/15]), and the remaining 8 studies had scores of 4 or less, which is considered (poor; 53% [8/15]). A total of 1316 participants received telerehabilitation intervention in the selected studies, where knee and hip replacement were 75% of all the studies. Strong and moderate grades of evidence (grade of recommendation A–B) were found in knee and hip replacement interventions. Studies on the upper limb were 25% of the studies, but only 1 study presented a moderate grade of evidence (grade of recommendation B) and the rest were of poor methodological quality with weak evidence (grade of recommendation C).ConclusionsConclusive evidence on the efficacy of telerehabilitation for treatment after an orthopedic surgery, regardless of pathology, was not obtained. We found strong evidence in favor of telerehabilitation in patients following total knee and hip arthroplasty and limited evidence in the upper limb interventions (moderate and weak evidence). Future research needs to be more extensive and conclusive. To the best of the authors’ knowledge, this is the first attempt at evaluating the quality of telerehabilitation intervention research after surgical procedures on orthopedic conditions in a systematic review. Clinical messages and future research recommendations are included in the review.

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Section: Introductionmentioning

confidence: 99%

Evidence of Benefit of Telerehabitation After Orthopedic Surgery: A Systematic Review

et al. 2017

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“…6 The use of computer-aided rehabilitation systems has attracted much attention from the research community. [7][8][9][10][11][12] Not so long ago, the main inconvenience that had to be addressed when automating any aspect related to human kinetics was the high cost involved in the required hardware and the participation of trained staff. This barrier has been eliminated to some extent since Microsoft Kinect was launched for the video game industry in 2010 and the release of a software development kit (SDK) that opened up the possibility of developing new applications based on it.…”

Section: Introductionmentioning

confidence: 99%

A computer-vision-based system for at-home rheumatoid arthritis rehabilitation

Dorado

García

Santofimia

et al. 2019

International Journal of Distributed Sensor Networks

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People suffering from rheumatoid arthritis are normally prescribed with physical exercises. The realization of these exercise routines should be supervised to prevent wrongly adopted postures or bad movements that end up harming the affected articulations. The motivational component associated with a supervised session is also important. On the contrary, having access to qualified supervision is not always possible due to different reasons, such as tight schedules, limited mobility, or economic reasons. This article presents a system for at-home rehabilitation, based on Microsoft Kinect, that resembles the role played by the physiotherapist during the supervision of an exercise session. The system not only supervises the realization of exercises but it also collects and manages information about the exercise-session history. Additionally, and despite affecting young people, rheumatoid arthritis is a disease that mainly affects seniors. For this reason, the system provides a natural interface to ease the interaction. Finally, the performance of the proposed system has been evaluated using a three-dimensional dynamic measurement system as a gold standard to validate the accuracy of the measurements. Obtained results demonstrate the potential of the Kinect-based system for at-home rehabilitation exercise routines.

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“…FEMO [11] is a human motion detection system based on RFID, which uses the backscattering signal of the passive RFID tag installed on the training equipment to detect the motion state of the user. eFisio-Track [12] is a telemedicine assistance system that detects patients' rehabilitation training movements by using the accelerometer equipment.…”

Section: Device-based Human Motion Recognitionmentioning

confidence: 99%

CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

Hao

Duan

Dang

et al. 2020

Mobile Information Systems

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WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

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eFisioTrack: A Telerehabilitation Environment Based on Motion Recognition Using Accelerometry

Cited by 11 publications

References 29 publications

Evidence of Benefit of Telerehabitation After Orthopedic Surgery: A Systematic Review

Evidence of Benefit of Telerehabitation After Orthopedic Surgery: A Systematic Review

A computer-vision-based system for at-home rheumatoid arthritis rehabilitation

CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

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