ArticleSensors (Basel, Switzerland)2017
The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 39 citations in OpenAlex.
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- Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.Sensors (Basel, Switzerland) · 2024Article
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- Heart Rate Variability Code: Does It Exist and Can We Hack It?Bioengineering (Basel, Switzerland) · 2023Review
- DDM-HSA: Dual Deterministic Model-Based Heart Sound Analysis for Daily Life Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Multi-dimensional task recognition for human-robot teaming: literature review.Frontiers in robotics and AI · 2023Review
- Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?Sensors (Basel, Switzerland) · 2022Article
- SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies.Sensors (Basel, Switzerland) · 2022Article
- Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals.Sensors (Basel, Switzerland) · 2021Article
- Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm.Sensors (Basel, Switzerland) · 2021Article
- Cardio-Diagnostic Assisting Computer System.Diagnostics (Basel, Switzerland) · 2020Article
- Article
- Current Status and Prospects of Health-Related Sensing Technology in Wearable Devices.Journal of healthcare engineering · 2019Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.