Functional activity monitoring from wearable sensor data

Nawab, S. Hamid; Roy, Sayantee; Luca, Carlo J. De

doi:10.1109/iembs.2004.1403325

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…The muscular contraction is generated by a stimulus that propagates from the brain cortex to the target muscle as an electrical potential, named action potential (AP). sEMG signal is frequently used for the evaluation of muscle functionality and activity, thanks to the non-invasiveness and ease of this technique [3,4,5]. Common applications are fatigue analysis [6] of rehabilitation exercises [5,7], postural control [8], musculoskeletal disorder analysis [9], gait analysis [10], movement recognition [11], gesture recognition [12], prosthetic control [13,14,15], to cite only a few.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Manoni

Turchetti

Falaschetti

et al. 2019

Sensors

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Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Manoni

Turchetti

Falaschetti

et al. 2019

Sensors

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“…With the above considerations in mind, combining sEMG and accelerometer sensors in a single device allows also to obtain all the necessary information for accurately examining muscle activity, force, fatigue, directionality and acceleration that are of essential importance in sports performance evaluation, injury prevention, rehabilitation, and human activity monitoring in general [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

Biagetti

Crippa

Falaschetti

et al. 2018

Sensors

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The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.

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“…The usual tremor parameters such as amplitude and frequency have been extracted from ACC and SEMG to distinguish physiological tremor from early stage and low amplitude pathologic tremor [Raethjen et al, 2004] [Bonato et al, 2004] and to identify functional activity [Nawab et al, 2004].…”

Section: Combination Of Acc and Semgmentioning

confidence: 99%

Sensing of pathological tremor using surface electromyography and accelerameter for real-time attenuation

Ferdinan¹

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Functional activity monitoring from wearable sensor data

Cited by 14 publications

References 10 publications

A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

Sensing of pathological tremor using surface electromyography and accelerameter for real-time attenuation

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