Human Activity Recognition Using A Single Optical Heart Rate Monitoring Wristband Equipped with Triaxial Accelerometer

Mehrang, Saeed; Pietilä, Julia; Tolonen, Johanna; Helander, Elina; Jimison, Holly; Pavel, Misha; Korhonen, Ilkka

doi:10.1007/978-981-10-5122-7_147

Cited by 15 publications

(17 citation statements)

References 13 publications

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“…The recognized activities were typical home-specific daily tasks including: (1) sitting (sitting still, sitting and drinking and sitting and doing math without pen and paper); (2) standing still; (3) household activities (two types of dish washing and two types of table cleaning); (4) low-intensity stationary cycling at 60 rpm; and (5) and high-intensity stationary cycling at 90 rpm [6]. All the activities were performed in a home-like apartment, which provided more realistic results over laboratory environments.…”

Section: Discussionmentioning

confidence: 99%

“…Absolute values were used since the subjects were wearing the device on either the left or right wrists depending on which one was their dominant hand. From the three ACC axes, signal magnitude area (SMA), mean, standard deviation, median, mean absolute deviation and the median absolute deviation of signal magnitude vector (SMV), as well as correlations between each pair of the three axes were calculated [6]. In the frequency domain, the relative energy of each axis in different frequency bands

((0 - 1], (1 - 2], (2 - 3], (3 - 4], (4 - 5], (5 - 6], (6 - 7], (7 - 8], (8 - 9], (9 - 10], (10 - 12.5)

Hz) was computed for each window using short-time Fourier transform (STFT).…”

Section: Methodsmentioning

confidence: 99%

“…From the HR signal, time domain features including mean, standard deviation, mean difference of consecutive windows, mean difference of every second window, mean absolute deviation and median absolute deviation were extracted [6]. …”

Section: Methodsmentioning

confidence: 99%

“…However, in many studies, researchers have tested multi-sensor configuration or less compliant measurement sites such as hip, ankle, chest and waist, which are not practical for long-term use [6]. Wrist-based activity recognition using currently available smartwatches and wristbands seems more feasible and user friendly for long-term applications [3].…”

Section: Introductionmentioning

confidence: 99%

“…Wrist-based activity recognition using currently available smartwatches and wristbands seems more feasible and user friendly for long-term applications [3]. Detecting physical activities by means of wrist-worn devices is, however, more challenging due to the wide degree of freedom of hand gesticulations [6]. That is, the movement pattern of the hand varies widely and may look similar while different types of activities are carried out.…”

Section: Introductionmentioning

confidence: 99%

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An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

Mehrang

Pietilä

Korhonen

2018

Sensors

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Wrist-worn sensors have better compliance for activity monitoring compared to hip, waist, ankle or chest positions. However, wrist-worn activity monitoring is challenging due to the wide degree of freedom for the hand movements, as well as similarity of hand movements in different activities such as varying intensities of cycling. To strengthen the ability of wrist-worn sensors in detecting human activities more accurately, motion signals can be complemented by physiological signals such as optical heart rate (HR) based on photoplethysmography. In this paper, an activity monitoring framework using an optical HR sensor and a triaxial wrist-worn accelerometer is presented. We investigated a range of daily life activities including sitting, standing, household activities and stationary cycling with two intensities. A random forest (RF) classifier was exploited to detect these activities based on the wrist motions and optical HR. The highest overall accuracy of 89.6 ± 3.9% was achieved with a forest of a size of 64 trees and 13-s signal segments with 90% overlap. Removing the HR-derived features decreased the classification accuracy of high-intensity cycling by almost 7%, but did not affect the classification accuracies of other activities. A feature reduction utilizing the feature importance scores of RF was also carried out and resulted in a shrunken feature set of only 21 features. The overall accuracy of the classification utilizing the shrunken feature set was 89.4 ± 4.2%, which is almost equivalent to the above-mentioned peak overall accuracy.

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Section: Discussionmentioning

confidence: 99%

((0 - 1], (1 - 2], (2 - 3], (3 - 4], (4 - 5], (5 - 6], (6 - 7], (7 - 8], (8 - 9], (9 - 10], (10 - 12.5)

Hz) was computed for each window using short-time Fourier transform (STFT).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

Mehrang

Pietilä

Korhonen

2018

Sensors

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Simultaneous Measurements Reading from More Than One MiBand 3 Wristbands

Rodak

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Mielnik

et al. 2022

Intelligent Sustainable Systems

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Discrimination of cycling patterns using accelerometric data and deep learning techniques

Procházka

Charvátová

Vyšata

et al. 2020

Neural Comput & Applic

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The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the contribution to this topic by (i) analysis of accelerometric signals recorded by wearable sensors located at specific body positions and by (ii) implementation of deep learning methods to classify signal features. This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system (GPS). The experimental dataset includes 850 observations that were recorded by a mobile device in the spine area (L3 verterbra) for cycling routes with the different slope. The proposed methodology includes the use of deep learning convolutional neural networks with five layers applied to signal values transformed into the frequency domain without A. Procházka ( )

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Human Activity Recognition Using A Single Optical Heart Rate Monitoring Wristband Equipped with Triaxial Accelerometer

Cited by 15 publications

References 13 publications

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

Simultaneous Measurements Reading from More Than One MiBand 3 Wristbands

Discrimination of cycling patterns using accelerometric data and deep learning techniques

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