A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring

By Che-Chang Yang, Yeh-Liang Hsu. Italian National Conference on Sensors, 2010

1,092 citations recorded by the source corpus.

Characteristics of physical activity are indicative of one’s mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/2d940733bea016ad47b17963735be25a70152ce8

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Physical Therapy, Sports Therapy and Rehabilitation

Citations in the corpus

The Rise of Consumer Health Wearables: Promises and Barriers (2016, background): “What can consumer wearables do?Heart rate can be measured with an oximeter built into a ring [3], muscle activity with an electromyographic sensor embedded into clothing [4], stress with an electodermal sensor incorporated into a wristband [5], and physical activity or sleep patterns via an accelero”
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, background): “Indeed, waist-placement of wearable sensors can better represent most human motions because they are then close to the center of mass of the human body [73].”
Detecting Falls with Wearable Sensors Using Machine Learning Techniques (2014, neutral): “Fall detection is surveyed in [9,10].”
Training Load Monitoring in Team Sports: A Novel Framework Separating Physiological and Biomechanical Load-Adaptation Pathways (2017, background): “Yang CC, Hsu YL.”
Physical Human Activity Recognition Using Wearable Sensors (2015, background): “…signals are divided into ﬁxed-length windows; (ii) event-deﬁned windows, where pre-processing is necessary to locate speciﬁc events, which are further used to deﬁne successive data partitioning and (iii) activity-deﬁned windows where data partitioning is based on the detection of activity changes.”
Daily physical activity assessment with accelerometers: new insights and validation studies (2013, background): “The Dynastream AMP-331 is a triaxial accelerometer (Cochrane, Alberta, Canada) positioned on the back of the ankle which measures the forward and vertical accelerations to determine the position of the foot in space (20,21).”
Mechanoacoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch (2019, neutral): “…capabilities in this context, with demonstrated examples in capturing signatures of cardiac mechanics such as seismocardiograms or ballistocardiograms 11–14 , respiratory rate and sounds 15–21 , sounds of swallowing 22–25 , vocals 26,27 , changes in body position and motion 28–30 and others 31 .”
Full body gait analysis with Kinect (2012, background): “Recent studies proposed the use of wearable sensors [10], [11].”