Trial reportSensors (Basel, Switzerland)2018
Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach.
Trial report in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- A novel device for detecting anaerobic threshold using sweat lactate during exercise.Scientific reports · 2021Trial
- Objective classification and scoring of movement deficiencies in patients with anterior cruciate ligament reconstruction.PloS one · 2019Trial
- A Functional Sensor-to-Segment Calibration Method Reduces the Effects of Varied Sensor Placement on Estimates of Segment Angular Excursion.Journal of applied biomechanics · 2026Article
- Article
- Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals.Sensors (Basel, Switzerland) · 2024Article
- AI-Driven Sensing Technology: Review.Sensors (Basel, Switzerland) · 2024Review
- Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study.JMIR formative research · 2024Article
- Longitudinal Monitoring of Biomechanical and Psychological State in Collegiate Female Basketball Athletes Using Principal Component Analysis.Translational sports medicine · 2024Article
- Anaerobic threshold using sweat lactate sensor under hypoxia.Scientific reports · 2023Article
- Posture Classification with a Bed-Monitoring System Using Radio Frequency Identification.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning-Enhanced Flexible Mechanical Sensing.Nano-micro letters · 2023Review
- Inertial Measurement Units and Application for Remote Health Care in Hip and Knee Osteoarthritis: Narrative Review.JMIR rehabilitation and assistive technologies · 2022Review
- Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed.Sensors (Basel, Switzerland) · 2022Article
- Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables' Data from the Crowd.Sensors (Basel, Switzerland) · 2022Article
- Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?Journal of diabetes science and technology · 2021Article
- Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis-A Scoping Review.Sensors (Basel, Switzerland) · 2020Article
- Artificial Neural Networks in Motion Analysis-Applications of Unsupervised and Heuristic Feature Selection Techniques.Sensors (Basel, Switzerland) · 2020Article
- A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors.Sensors (Basel, Switzerland) · 2020Article
- Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network.Frontiers in bioengineering and biotechnology · 2020Article
- Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual's typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual's movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy.
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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.