Rehabilitation motion recognition based on the international biomechanical standards

Gama, Alana Elza Fontes da; Chaves, Thiago; Fallavollita, Pascal; Figueiredo, Lucas; Teichrieb, Verônica

doi:10.1016/j.eswa.2018.09.026

Cited by 25 publications

(16 citation statements)

References 11 publications

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“…The participants were instructed to perform the five standard lumbar-pelvic movements based on the low back pain patients' rehabilitation assessment process [5]. These five lumbar-pelvic movements follow the biomechanical standards and widely used by medical experts to assess the lumbar movements performed by different individuals [75]). Each participant performed each movement for 5 times and repeated the entire experiment for 3 times.…”

Section: Study Design and Experimental Proceduresmentioning

confidence: 99%

Electronic Skin Wearable Sensors for Detecting Lumbar–Pelvic Movements

Zhang

Haghighi

Burstein

et al. 2020

Sensors

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Background: A nanomaterial-based electronic-skin (E-Skin) wearable sensor has been successfully used for detecting and measuring body movements such as finger movement and foot pressure. The ultrathin and highly sensitive characteristics of E-Skin sensor make it a suitable alternative for continuously out-of-hospital lumbar–pelvic movement (LPM) monitoring. Monitoring these movements can help medical experts better understand individuals’ low back pain experience. However, there is a lack of prior studies in this research area. Therefore, this paper explores the potential of E-Skin sensors to detect and measure the anatomical angles of lumbar–pelvic movements by building a linear relationship model to compare its performance to clinically validated inertial measurement unit (IMU)-based sensing system (ViMove). Methods: The paper first presents a review and classification of existing wireless sensing technologies for monitoring of body movements, and then it describes a series of experiments performed with E-Skin sensors for detecting five standard LPMs including flexion, extension, pelvic tilt, lateral flexion, and rotation, and measure their anatomical angles. The outputs of both E-Skin and ViMove sensors were recorded during each experiment and further analysed to build the comparative models to evaluate the performance of detecting and measuring LPMs. Results: E-Skin sensor outputs showed a persistently repeating pattern for each movement. Due to the ability to sense minor skin deformation by E-skin sensor, its reaction time in detecting lumbar–pelvic movement is quicker than ViMove by ~1 s. Conclusions: E-Skin sensors offer new capabilities for detecting and measuring lumbar–pelvic movements. They have lower cost compared to commercially available IMU-based systems and their non-invasive highly stretchable characteristic makes them more comfortable for long-term use. These features make them a suitable sensing technology for developing continuous, out-of-hospital real-time monitoring and management systems for individuals with low back pain.

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Section: Study Design and Experimental Proceduresmentioning

confidence: 99%

Electronic Skin Wearable Sensors for Detecting Lumbar–Pelvic Movements

Zhang

Haghighi

Burstein

et al. 2020

Sensors

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“…Repetitive task-oriented movements are the single most important variable of cardiac rehabilitation and have a therapeutic gain; the literature suggests that an elderly patient recovering from cardiac failure must play an active role in the rehabilitation process if improvement is to occur (9). Several robotic systems designed for rehabilitation are presented in the scientific literature as prototypes while others are being on the market, already.…”

Section: A Survey Of Rehabilitation Robotic Systems and Their Adequacmentioning

confidence: 99%

“…-Assistive robotics: refer to secondary aspects related to the medical process, providing assistance to the healthcare givers of the patients. The scientific literature has demonstrated that physical exercise training improves parameters like aerobic capacity and muscular strength in cardiac patients (9). Rehabilitation of cardiac patients has always been a great challenge, due to the complexity of the recovery treatment and so there has been a strong motivation to the development of new systems for increasing the efficiency of rehabilitation procedures (10).…”

Section: Introductionmentioning

confidence: 99%

Cardiac rehabilitation for older patients with cardiovascular pathology using robotic systems – A survey

Mocan¹,

Pharmacy²,

Mocan³

2019

Balneo

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In the last decades, we have witnessed a drastic increase in the proportion of elders in the western countries, and more than a third of worldwide population dies because of cardiovascular diseases. The elderly patients with cardiovascular diseases, but not only, are frail and need constant assistance, and so the healthcare system will slowly become incapable of properly address all the patients' medical problems and rehabilitation issues, while aiming towards a continuous increase of the life quality. The rehabilitation robotic systems have the potential to stimulate and support the re-learning of the functional motor pattern of over-ground walking in an optimal manner while increasing patient's motivation, which constitutes a crucial element in the subject's cardiac recovery. Thus, the present article aims at reviewing the robotic systems available for cardiac rehabilitation in elders with cardiac pathology.

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“…The depth camera Kinect has brought rapid development to vision-based rehabilitation action recognition and assessment of the recovery status [13][14][15]. Alana et al [16] extracted skeleton joint points through Kinect and combined the joint angles to recognize and evaluate stroke patients' actions. He et al [17] utilized the long short-term memory (LSTM) model to predict the intentions of stroke patients' lower limbs and validate the feasibility of Kinect.…”

Section: Introductionmentioning

confidence: 99%

Testing the Feasibility of a Multi-Model Fusion Method for Monitoring the Action of Rehabilitating Stroke Patients in Care Management

et al. 2021

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Post-stroke care encounters challenges, including high cost, lack of professionals, and insufficient rehabilitation state evaluation. Computer technology can alleviate these issues, as it allows health care professionals (HCP) to quantify the workload and thus enhance rehabilitation care quality. In this paper, a novel multi-model fusion method, terms as pose dual-stream network (PDSN), is devised, aiming to test the feasibility of monitoring the training actions of rehabilitating stroke patients in care management. In particular, this deep-learning-based algorithm combines human pose estimation and dual-stream networks in an innovative way. We utilize an improved OpenPose to estimate human pose from videos obtained by the low-cost monocular camera. In dual-stream networks, the spatial and motion streams are flexibly integrated. The spatial stream network combines the Gated Recurrent Unit (GRU) and attention mechanism to extract spatiotemporal data, while the motion stream network is composed of improved multi-layer 1D Convolutional Neural Networks (CNN), which enhanced by causal and dilated convolution skillfully. Additionally, an adaptive weight fusion strategy is used to fuse the two networks for the final action classification. Results show high accuracy on two public datasets and a dataset created by us, which validate the superiority and feasibility of our method.

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Rehabilitation motion recognition based on the international biomechanical standards

Cited by 25 publications

References 11 publications

Electronic Skin Wearable Sensors for Detecting Lumbar–Pelvic Movements

Electronic Skin Wearable Sensors for Detecting Lumbar–Pelvic Movements

Cardiac rehabilitation for older patients with cardiovascular pathology using robotic systems – A survey

Testing the Feasibility of a Multi-Model Fusion Method for Monitoring the Action of Rehabilitating Stroke Patients in Care Management

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