Physical activity profiling: Activity-specific step counting and energy expenditure models using 3D wrist acceleration

Delgado-Gonzalo, Ricard; Celka, P.; Renevey, Ph.; Dasen, S.; Solà, Josep; Bertschi, Mattia; Lemay, Mathieu

doi:10.1109/embc.2015.7320271

Cited by 13 publications

(5 citation statements)

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“…In Ref. [20], the authors divided the movement into rhythmic and nonrhythmic activities by collecting the triaxial acceleration data of the wrist. When the oscillation meets the requirements of a certain length, intensity, and shape, it is considered as step counting data.…”

Section: State-of-the-art Methodsmentioning

confidence: 99%

An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting

Sun

Chen

Zhang

2021

Discrete Dynamics in Nature and Society

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With the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an intelligent optimization strategy based on deep reinforcement learning is proposed. In this study, the counting problem is transformed into a serialization decision optimization. This study integrates the noise recognition and the user feedback to update parameters. The end-to-end processing is direct, which alleviates the inaccuracy of step counting in the follow-up step counting module caused by the inaccuracy of noise filtering in the two-stage processing and makes the model parameters continuously updated. Finally, the experimental results show that the proposed model achieves superior performance to existing approaches.

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Section: State-of-the-art Methodsmentioning

confidence: 99%

An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting

Sun

Chen

Zhang

2021

Discrete Dynamics in Nature and Society

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“…Considering the above limitations, an alternative for PA monitoring, particularly gait, is to use a single IMU mounted on the wrist [42]- [53]. It offers comfort, high usability and discreet monitoring (e.g.…”

Section: Introductionmentioning

confidence: 99%

Real-World Gait Bout Detection Using a Wrist Sensor: An Unsupervised Real-Life Validation

et al. 2020

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Gait bouts (GB), as a prominent indication of physical activity, contain valuable fundamental information closely associated with human's health status. Therefore, objective assessment of the GB (e.g. detection, spatio-temporal analysis) during daily life is very important. A feasible and effective way of GB detection in real-world situations is using a wrist-mounted inertial measurement unit. However, the high degree of freedom of the wrist movements during daily-life situations imposes serious challenges for a precise and robust automatic detection. In this study, we deal with such challenges and propose an accurate algorithm to detect GB using a wrist-mounted accelerometer. Features, derived based on biomechanical criteria (intensity, periodicity, posture, and other non-gait dynamicity), along with a Bayes estimator followed by two physically-meaningful post-classification procedures are devised to optimize the performance. The proposed method has been validated against a shank-based reference algorithm on two datasets (29 young and 37 elderly healthy people). The method has achieved a high median [interquartile range] of 90.

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“…Generally, an accelerometer is used for PAI estimation [5]- [9]. To improve the estimation accuracy, a physical activity (PA) classification algorithm incorporating heart rate and tri-acceleration signals was proposed in the previous work [10].…”

Section: Introductionmentioning

confidence: 99%

A 11.3-µA Physical Activity Monitoring System Using Acceleration and Heart Rate

Nakanishi

Izumi

Tsukahara

et al. 2018

IEICE Trans. Electron.

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This paper presents an algorithm for a physical activity (PA) classification and metabolic equivalents (METs) monitoring and its System-on-a-Chip (SoC) implementation to realize both power reduction and high estimation accuracy. Long-term PA monitoring is an effective means of preventing lifestyle-related diseases. Low power consumption and long battery life are key features supporting the wider dissemination of the monitoring system. As described herein, an adaptive sampling method is implemented for longer battery life by minimizing the active rate of acceleration without decreasing accuracy. Furthermore, advanced PA classification using both the heart rate and acceleration is introduced. The proposed algorithms are evaluated by experimentation with eight subjects in actual conditions. Evaluation results show that the root mean square error with respect to the result of processing with fixed sampling rate is less than 0.22 [METs], and the mean absolute error is less than 0.06 [METs]. Furthermore, to minimize the system-level power dissipation, a dedicated SoC is implemented using 130-nm CMOS process with FeRAM. A nonvolatile CPU using non-volatile memory and a flip-flop is used to reduce the stand-by power. The proposed algorithm, which is implemented using dedicated hardware, reduces the active rate of the CPU and accelerometer. The current consumption of the SoC is less than 3-µA. And the evaluation system using the test chip achieves 74% system-level power reduction. The total current consumption including that of the accelerometer is 11.3-µA on average.

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Physical activity profiling: Activity-specific step counting and energy expenditure models using 3D wrist acceleration

Cited by 13 publications

References 14 publications

An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting

An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting

Real-World Gait Bout Detection Using a Wrist Sensor: An Unsupervised Real-Life Validation

A 11.3-µA Physical Activity Monitoring System Using Acceleration and Heart Rate

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