ArticleJMIR mHealth and uHealth2021
The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study.
Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Wearable Devices for Remote Monitoring of Chronic Diseases: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- Determination of cut-off points for the Move4 accelerometer and assessment of energy expenditure in children and adolescents aged 6-16 years using manual wheelchairs: a validation and calibration study.Journal of neuroengineering and rehabilitation · 2026Article
- Body placement of inertial measurement units differentially affects physical activity assessment accuracy in drug-naïve Parkinson's disease.Scientific reports · 2026Article
- A comprehensive IMU dataset for evaluating sensor layouts in human activity and intensity recognition.Scientific data · 2026Article
- Validity and Reliability of Using a Belt-Worn Accelerometer on the Lower Back to Monitor Physical Activity.Sensors (Basel, Switzerland) · 2026Article
- Energy-Expenditure Estimation During Aerobic Training Sessions for Badminton Players.Sensors (Basel, Switzerland) · 2025Article
- Noninvasive Multiparameter Monitoring for the Detection of Decompensated Heart Failure: Exploratory Study.JMIR formative research · 2025Article
- Enhanced Detection and Segmentation of Sit Phases in Patients with Parkinson's Disease Using a Single SmartWatch and Random Forest Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions.Sensors (Basel, Switzerland) · 2024Article
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- Article
- Measuring gait parameters from a single chest-worn accelerometer in healthy individuals: a validation study.Scientific reports · 2024Article
- A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor.Sensors (Basel, Switzerland) · 2024Article
- The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer.Biomedical engineering online · 2024Article
- HARE: Unifying the Human Activity Recognition Engineering Workflow.Sensors (Basel, Switzerland) · 2023Article
- Minimum number of inertial measurement units needed to identify significant variations in walk patterns of overweight individuals walking on irregular surfaces.Scientific reports · 2023Article
- Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study.JMIR human factors · 2023Article
- Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model.Sensors (Basel, Switzerland) · 2023Article
- Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method.Bioengineering (Basel, Switzerland) · 2022Article
- Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundResearch has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors.
objectiveThis study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults.
methodsIn total, 93 participants (mean age 72.2 years, SD 7.1) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary versus nonsedentary, locomotion versus nonlocomotion, and lifestyle versus nonlifestyle activities (eg, leisure walk vs computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on 5 different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used to develop random forest models to assess activity category recognition accuracy and MET estimation.
resultsModel performance for both MET estimation and activity category recognition were strengthened with the use of additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03-0.09 MET increase in prediction error compared with wearing all 5 devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for the detection of locomotion (balanced accuracy decrease range 0-0.01), sedentary (balanced accuracy decrease range 0.05-0.13), and lifestyle activities (balanced accuracy decrease range 0.04-0.08) compared with all 5 placements. The accuracy of recognizing activity categories increased with additional placements (accuracy decrease range 0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition.
conclusionsAdditional accelerometer devices slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.
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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.