Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data

Barut, Onur; Zhou, Li; Luo, Yan

doi:10.1109/jiot.2020.2996578

Cited by 70Context-Aware Activity Recognition SystemsPhysical Activity and HealthTime Series Analysis and Forecasting

Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data

Barut, Onur, Zhou, Li, Luo, Yan·2020·IEEE Internet of Things Journal

DOI 10.1109/jiot.2020.2996578

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Abstract

Human activity recognition (HAR) and measuring the intensity of activity are increasingly important for healthcare applications, such as fitness tracking and patient monitoring. However, these two tasks have been performed separately, leading to delays and expensive implementations. In this article, we propose a holistic approach to achieve both HAR and intensity estimation simultaneously. We introduce a new data set with both activity types and activity intensities, and a multitask long short-term memory (LSTM) model to accurately classify the activity types and estimate the intensity of each activity. In addition, we evaluate our proposed neural network with other publicly available data sets and show that including activity intensities in the data set help multitask models perform comparably to two separate single-task models.

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Multitask LSTM is developed in [209] to first extract features with shared weight, and then classify activities and estimate intensity in separate branches.
“Moreover, Multitask LSTM is developed in [209] to first extract features with shared weight, and then classify activities and estimate intensity in separate branches.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [209]

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Authors: Barut, O., Zhou, L., & Luo, Y.
Affiliations: University of Massachusetts Lowell (US)
ORCID: Barut, Onur
Zhou, Li
Luo, Yan
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-09
Volume: 7
Issue: 9
Pages: 8760-8768
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2020.2996578
OpenAlex: W3027738413
Cited by: 70
References: 27
Citations by year: 2026: 4 · 2025: 10 · 2024: 14 · 2023: 16 · 2022: 11 · 2021: 12 · 2020: 3
FWCI: 4.08
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (am)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Internet of Things Journal (publishedVersion)
Funders: National Science Foundation of USA (1547428, 1738965, 1450996)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Health Sciences › Medicine › Physiology › Physical Activity and Health
Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords: human activity recognition, multi-task learning, long short-term memory, wearable sensors, activity dataset, fitness tracking, patient monitoring, medical applications

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Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data

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