Wearable Wireless Sensors for Rehabilitation

Dorsch, Andrew; King, Christine; Dobkin, Bruce H.

doi:10.1007/978-3-319-28603-7_29

Cited by 4 publications

(2 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the technology of AR/VR systems continues to improve, additional clinical studies are needed to generate the evidence base demonstrating the utility and efficacy of such systems for clinical care and research in rehabilitation. 26…”

Section: Virtual and Augmented Reality Systemsmentioning

confidence: 99%

Wearable technologies for active living and rehabilitation: Current research challenges and future opportunities

Rodgers

Alon

Pai³

et al. 2019

Journal of Rehabilitation and Assistive Technologies Engineerin

View full text Add to dashboard Cite

This paper presents some recent developments in the field of wearable sensors and systems that are relevant to rehabilitation and provides examples of systems with evidence supporting their effectiveness for rehabilitation. A discussion of current challenges and future developments for selected systems is followed by suggestions for future directions needed to advance towards wider deployment of wearable sensors and systems for rehabilitation.

show abstract

Section: Virtual and Augmented Reality Systemsmentioning

confidence: 99%

Wearable technologies for active living and rehabilitation: Current research challenges and future opportunities

Rodgers

Alon

Pai³

et al. 2019

Journal of Rehabilitation and Assistive Technologies Engineerin

View full text Add to dashboard Cite

show abstract

“…Also, monitoring patients' locomotion with neurological diseases such as Parkinson's disease in clinics does not always provide the doctors with a valid and accurate data due to the fact that patients always know that they are being monitored. So, the normal symptoms may not occur as they happen in normal days [5,6].…”

Section: Introductionmentioning

confidence: 99%

Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

et al. 2019

View full text Add to dashboard Cite

Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.

show abstract

Real-Time Human Activity Recognition Using Textile-Based Sensors

Ayvaz

Elmoughni

Atalay

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Real-time human activity recognition is a popular and challenging topic in sensor systems. Inertial measurement units, vision-based systems, and wearable sensor systems are mostly used for gathering motion data. However, each system has drawbacks such as drift error, illumination, occlusion, etc. Therefore, under certain circumstances, they are not efficient alone in activity estimation. To overcome this, hybrid sensor systems were used as an alternative approach in the last decade. In this study, a human activity recognition system is proposed using textile-based capacitive sensors. The aim of the system is to recognize the basic human actions in real-time such as walking, running, squatting, and standing. The sensor system proposed in this study is used to collect human activity data from the participants with different anthropometrics and create an activity recognition system. The performance of the machine learning models is tested on unseen activity data. The obtained results showed the effectiveness of our approach by achieving high accuracy up to 83.1% on selected human activities in real-time.

show abstract

Wearable Wireless Sensors for Rehabilitation

Cited by 4 publications

References 78 publications

Wearable technologies for active living and rehabilitation: Current research challenges and future opportunities

Wearable technologies for active living and rehabilitation: Current research challenges and future opportunities

Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

Real-Time Human Activity Recognition Using Textile-Based Sensors

Contact Info

Product

Resources

About