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The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/clc.4960130809openalex-snapshot
Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity
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A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction
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Feature Selection and Activity Recognition from Wearable Sensors
inferredcites →https://doi.org/10.1007/978-3-319-00846-2_374openalex-snapshot
Wrist-Worn Accelerometer to Detect Postural Transitions and Walking Patterns
inferredcites →https://doi.org/10.1007/s00520-009-0636-0openalex-snapshot
Patterns of circadian activity rhythms and their relationships with fatigue and anxiety/depression in women treated with breast cancer adjuvant chemotherapy
inferredcites →https://doi.org/10.1007/s11556-013-0132-xopenalex-snapshot
Accelerometry analysis of physical activity and sedentary behavior in older adults: a systematic review and data analysis
inferredcites →https://doi.org/10.1007/s40279-017-0716-0openalex-snapshot
Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations
inferredcites →https://doi.org/10.1016/b978-0-12-374388-6.00026-0openalex-snapshot
Tables
inferredcites →https://doi.org/10.1016/j.conctc.2017.02.003openalex-snapshot
Metabolic costs of daily activity in older adults (Chores XL) study: Design and methods
inferredcites →https://doi.org/10.1016/j.medengphy.2014.02.006openalex-snapshot
Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities
inferredcites →https://doi.org/10.1016/j.medengphy.2014.02.012openalex-snapshot
Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems
inferredcites →https://doi.org/10.1016/j.nmd.2015.06.442openalex-snapshot
Wrist-worn accelerometer as innovative tool for longitudinal follow-up of idiopathic inflammatory myopathy patients: A pilot study
inferredcites →https://doi.org/10.1016/j.pedn.2010.11.004openalex-snapshot
Validation of a Hip-Worn Accelerometer in Measuring Sleep Time in Children
inferredcites →https://doi.org/10.1016/j.physio.2016.10.077openalex-snapshot
Criterion validity of an ankle or waist mounted Actigraph GT3X accelerometer in measurement of body position and step count
inferredcites →https://doi.org/10.1016/j.sleh.2017.12.007openalex-snapshot
Validation of a physical activity accelerometer device worn on the hip and wrist against polysomnography
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1055/s-2001-13816openalex-snapshot
Validation of the COSMED K4 b2 Portable Metabolic System
inferredcites →https://doi.org/10.1056/nejm199503023320902openalex-snapshot
Lower-Extremity Function in Persons over the Age of 70 Years as a Predictor of Subsequent Disability
inferredcites →https://doi.org/10.1080/02640414.2018.1449091openalex-snapshot
Estimating physical activity in youth using an ankle accelerometer
inferredcites →https://doi.org/10.1080/03091902.2018.1470692openalex-snapshot
A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals
inferredcites →https://doi.org/10.1088/0967-3334/35/11/2191openalex-snapshot
A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers
inferredcites →https://doi.org/10.1109/icdar.1995.598994openalex-snapshot
Random decision forests
inferredcites →https://doi.org/10.1111/cpf.12337openalex-snapshot
Physical activity using wrist‐worn accelerometers: comparison of dominant and non‐dominant wrist
inferredcites →https://doi.org/10.1111/cpf.12538openalex-snapshot
A comparison of physical activity from Actigraph GT 3X+ accelerometers worn on the dominant and non‐dominant wrist
inferredcites →https://doi.org/10.1111/j.1399-5618.2005.00187.xopenalex-snapshot
Actigraphic assessment of circadian activity and sleep patterns in bipolar disorder
inferredcites →https://doi.org/10.1111/sms.13017openalex-snapshot
Reliable recognition of lying, sitting, and standing with a hip‐worn accelerometer
inferredcites →https://doi.org/10.1123/jpah.9.3.389openalex-snapshot
Validity and Comparability of a Wrist-Worn Accelerometer in Children
inferredcites →https://doi.org/10.1152/japplphysiol.00026.2015openalex-snapshot
Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements
inferredcites →https://doi.org/10.1152/japplphysiol.00465.2009openalex-snapshot
An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer
inferredcites →https://doi.org/10.1177/0269215517712042openalex-snapshot
A study of predictive validity, responsiveness, and minimal clinically important difference of arm accelerometer in real-world activity of patients with chronic stroke
inferredcites →https://doi.org/10.1186/1478-7954-9-29openalex-snapshot
Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards
inferredcites →https://doi.org/10.1186/1479-5868-7-60openalex-snapshot
Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese U.S. men and women
inferredcites →https://doi.org/10.1186/1756-0500-3-149openalex-snapshot
Actigraphic registration of motor activity reveals a more structured behavioural pattern in schizophrenia than in major depression
inferredcites →https://doi.org/10.1249/01.mss.0000126805.32659.43openalex-snapshot
Evaluation of the SenseWear Pro Armband™ to Assess Energy Expenditure during Exercise
inferredcites →https://doi.org/10.1249/jes.0000000000000206openalex-snapshot
A Framework to Evaluate Devices That Assess Physical Behavior
inferredcites →https://doi.org/10.1249/mss.0000000000000597openalex-snapshot
Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living
inferredcites →https://doi.org/10.1249/mss.0000000000000804openalex-snapshot
Differentiating Sitting and Lying Using a Thigh-Worn Accelerometer
inferredcites →https://doi.org/10.1249/mss.0000000000001124openalex-snapshot
Comparison of Accelerometry Methods for Estimating Physical Activity
inferredcites →https://doi.org/10.1249/mss.0b013e3182399e5bopenalex-snapshot
Best Practices for Using Physical Activity Monitors in Population-Based Research
inferredcites →https://doi.org/10.1249/mss.0b013e31827f0d9copenalex-snapshot
Estimating Activity and Sedentary Behavior from an Accelerometer on the Hip or Wrist
inferredcites →https://doi.org/10.1249/mss.0b013e31829736d6openalex-snapshot
Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle
inferredcites →https://doi.org/10.1371/journal.pone.0093511openalex-snapshot
Accuracy of StepWatch™ and ActiGraph Accelerometers for Measuring Steps Taken among Persons with Multiple Sclerosis
inferredcites →https://doi.org/10.1371/journal.pone.0118642openalex-snapshot
Recognizing Complex Upper Extremity Activities Using Body Worn Sensors
inferredcites →https://doi.org/10.1371/journal.pone.0130851openalex-snapshot
Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body
inferredcites →https://doi.org/10.1371/journal.pone.0188215openalex-snapshot
Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults
inferredcites →https://doi.org/10.2196/11270openalex-snapshot
Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study
inferredcites →https://doi.org/10.2196/rehab.7317openalex-snapshot
Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models
inferredcites →https://doi.org/10.3109/07420528.2011.553016openalex-snapshot
Rest/Activity Rhythms and Cardiovascular Disease in Older Men
inferredcites →https://doi.org/10.3390/s100201154openalex-snapshot
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers
inferredcites →https://doi.org/10.3390/s151229858openalex-snapshot
Physical Human Activity Recognition Using Wearable Sensors
inferredcites →https://doi.org/10.4324/9781315071541openalex-snapshot
The Ageing and Development Report

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Advancing Health Analytics: Random Forest Regression Models for Caloric Expenditure in Varied Activities
inferred← citeshttps://doi.org/10.1007/978-3-032-28819-6_9openalex-snapshot
Synthetic Data–Guided Feature Selection for Robust Activity Recognition in Older Adults
inferred← citeshttps://doi.org/10.1007/978-3-032-39674-7_43openalex-snapshot
Analysis of Wearable Sensor Placement Impact on Human Activity Time Series Data
inferred← citeshttps://doi.org/10.1016/j.cjca.2024.12.011openalex-snapshot
Considerations for Assessing Physical Activity in Those at Risk of and Living With Cardiovascular Disease
inferred← citeshttps://doi.org/10.1016/j.cmpb.2026.109622openalex-snapshot
Activity contextualisation and terrain classification via wearables in Parkinson’s
inferred← citeshttps://doi.org/10.1038/s41597-026-06710-9openalex-snapshot
A comprehensive IMU dataset for evaluating sensor layouts in human activity and intensity recognition
inferred← citeshttps://doi.org/10.1038/s41598-023-43428-9openalex-snapshot
Minimum number of inertial measurement units needed to identify significant variations in walk patterns of overweight individuals walking on irregular surfaces
inferred← citeshttps://doi.org/10.1038/s41598-024-62330-6openalex-snapshot
Measuring gait parameters from a single chest-worn accelerometer in healthy individuals: a validation study
inferred← citeshttps://doi.org/10.1101/2023.04.21.537226openalex-snapshot
Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-worn Sensor: Performance Characterization Study
inferred← citeshttps://doi.org/10.1109/access.2023.3239265openalex-snapshot
Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes
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SynHAR: Augmenting Human Activity Recognition With Synthetic Inertial Sensor Data Generated From Human Surface Models
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Physical Locomotion Detection using pre- classification for Muscles monitoring
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A case study: effect of wrist sensor displacement on HAR performance using LSTM and attention mechanism
inferred← citeshttps://doi.org/10.1109/iciis69028.2026.11450804openalex-snapshot
A Scalable Wearable Wireless Sensor Network for Human Activity Detection
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Internet of Things Centric-Based Multiactivity Recognition in Smart Home Environment
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Characterization and Classification of Tactical Movements Using Wearable Motion Sensors and Deep Learning Models
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A Dual-Stream Fusion Network for Human Energy Expenditure Estimation with Wearable Sensor
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Application of Smart Insoles for Recognition of Activities of Daily Living: A Systematic Review
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HARNode: A Time-Synchronised, Open-Source, Multi-Device, Wearable System for Ad Hoc Field Studies
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Tolerability and the Accelerome: Open-Source Wrist Accelerometry Relates to Symptom Burden in Androgen Ablated Older Men with Prostate Cancer
inferred← citeshttps://doi.org/10.1177/20552076261498087openalex-snapshot
Agreement of a wearable activity monitor with reference devices in post-operative inpatients: A method-comparison study
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The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer
inferred← citeshttps://doi.org/10.1186/s12984-022-00992-xopenalex-snapshot
Assessing inertial measurement unit locations for freezing of gait detection and patient preference
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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
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Is Complexity of Daily Activity Associated with Physical Function and Life-Space Mobility among Older Adults?
inferred← citeshttps://doi.org/10.2196/48270openalex-snapshot
Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study
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Noninvasive Multiparameter Monitoring for the Detection of Decompensated Heart Failure: Exploratory Study
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Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care
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Wearable Devices for Remote Monitoring of Chronic Diseases: Systematic Review
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Wearable Devices for Remote Monitoring of Chronic Diseases: A Systematic Review (Preprint)
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Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method
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A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring
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A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home
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Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?
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Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model
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HARE: Unifying the Human Activity Recognition Engineering Workflow
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A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor
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Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions
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Enhanced Detection and Segmentation of Sit Phases in Patients with Parkinson’s Disease Using a Single SmartWatch and Random Forest Algorithms
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Energy-Expenditure Estimation During Aerobic Training Sessions for Badminton Players
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Validity and Reliability of Using a Belt-Worn Accelerometer on the Lower Back to Monitor Physical Activity
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Nurses on the Edge: An On-device Human Activity Recognition Framework that Optimizes the Sensor Placement
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Nurses on the Edge: An On-device Human Activity Recognition Framework that Optimizes the Sensor Placement
inferred← citeshttps://doi.org/10.48550/arxiv.2110.02768openalex-snapshot
Posture Recognition in the Critical Care Settings using Wearable Devices
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Evaluating the Correlation between Eyeglass-Type Wearable Device Measurements and Subjective Physical Activity Assessments
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