Assessing sedentary behavior using wearable devices: An overview and future directions

Sasai, Hiroyuki

doi:10.7600/jpfsm.6.135

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…Ambulation, fitness and daily activities were most commonly investigated. This is a general problem for all studies in this area and Sasai also confirmed it in his recent study [ 4 ]. Prior studies have been restricted to studies based on accelerometer mounted to the different body parts [ 41 ] or based on portable devices [ 38 ].…”

Section: Discussionmentioning

confidence: 53%

“…Sedentary behavior analysis has been explored using wrists, hips or thigh mounted accelerometers using elastic belts. Sasai [ 4 ] overview of assessment of sedentary behavior using wearable devices and concluded that most commonly used wearable devices were activPAL, ActiGraph, and Active style Pro. However, those devices are not integrated with clothes but rather attached to the body using elastic belts.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

Kańtoch

2018

Sensors

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With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.

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Section: Discussionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

Kańtoch

2018

Sensors

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“…Active Style Pro has been verified for reproducibility and reliability as previously described [ 33 , 34 ]. Furthermore, many of the SB studies in Japan use this tri-accelerometer [ 35 , 36 ]. Subjects were required to wear on their waist its tool from morning to night, except when sleeping; it was not possible to use while participating in underwater activities (bathing and swimming) and during sports activities that may involve contact.…”

Section: Methodsmentioning

confidence: 99%

Seasonal Changes in Continuous Sedentary Behavior in Community—Dwelling Japanese Adults: A Pilot Study

et al. 2020

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Background: Sedentary behavior (SB) is associated with adverse health outcomes. The aim of this study was to clarify seasonal changes in SB including continuous SB (CSB) in community-dwelling Japanese adults. Methods: In this secondary analysis, a total of 65 community-dwelling Japanese adults (7 men and 58 women, 69 (50–78) years) were enrolled. SB (%), including CSB (≥30 min) as well as physical activity, were evaluated using a tri-accelerometer. The differences in these parameters between baseline (summer) and follow-up (winter) were examined. Results: %CSB and %SB at baseline were 20.5 (4.0–60.9) and 54.0 ± 11.5, respectively. CSB was significantly increased (6.6%), and SB was also increased (5.1%) at follow-up compared with baseline. In addition, there were positive relationships between changes in CSB and SB, and body weight and body mass index. Conclusions: These results suggest that there were significant seasonal changes in CSB and SB in community-dwelling Japanese adults.

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“…Globally-accepted wearable devices to monitor sedentary behaviour are ActiGraph and actiPAL [ 23 ]. Matthews et al [ 24 ] used the ActiGraph device to record the acceleration information and estimate the body movement.…”

Section: Related Workmentioning

confidence: 99%

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

Fahim

Baker

Khattak

et al. 2018

Sensors

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Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active.

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Assessing sedentary behavior using wearable devices: An overview and future directions

Cited by 12 publications

References 38 publications

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

Seasonal Changes in Continuous Sedentary Behavior in Community—Dwelling Japanese Adults: A Pilot Study

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

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