SynthesisJournal of neuroengineering and rehabilitation2020
Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments.
Synthesis in Journal of neuroengineering and rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.
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.
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Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Wearable-Sensor-Based Physical Activity and Sleep in Children with Down Syndrome Aged 0-5 Years: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review.Journal of neuroengineering and rehabilitation · 2021Pooled it
- Minimal Sensor Configuration for Human Activity Recognition in Patients With Hip Osteoarthritis: Proof-of-Concept Study.JMIR biomedical engineering · 2026Article
- Body position classification using wearable sensors in infants with cerebral palsy.Infant behavior & development · 2026Article
- A Comparison of Machine Learning Approaches to Activity Classification Using IMU Data Collected in a Community-Based Setting.Sensors (Basel, Switzerland) · 2026Article
- Association of wearable sensor-based gait analysis with phenoconversion trajectories in idiopathic REM sleep behavior disorder.NPJ Parkinson's disease · 2026Article
- Clinical validation of a rule-based decision tree algorithm for classifying hip movements in people with spinal cord injury.The journal of spinal cord medicine · 2026Article
- Designing a Gait Recognition Algorithm for Older Adults Using Mobility Aids: Prospective Cohort Study.JMIR formative research · 2025Article
- L-Dopa Comparably Improves Gait and Limb Movements in Parkinson's Disease: A Wearable Sensor Analysis.Biomedicines · 2025Article
- Precision of an Inertial System to Evaluate the Finger Tapping Test in Women with Fibromyalgia.Sports (Basel, Switzerland) · 2025Article
- Advances and New Therapies in Traumatic Spinal Cord Injury.Journal of clinical medicine · 2025Review
- Better Understanding Rehabilitation of Motor Symptoms: Insights from the Use of Wearables.Pragmatic and observational research · 2025Review
- Article
- Wearable Technology Applications and Methods to Assess Clinical Outcomes in Foot and Ankle Disorders: Achievements and Perspectives.Sensors (Basel, Switzerland) · 2024Review
- Real-Time Sensing of Upper Extremity Movement Diversity Using Kurtosis Implemented on a Smartwatch.Sensors (Basel, Switzerland) · 2024Article
- Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study.JMIR medical informatics · 2024Article
- Article
- Assessment of Mobility Trajectories Using Wearable Inertial Sensors During Autologous Hematopoietic Cell Transplant.Archives of physical medicine and rehabilitation · 2024Article
- Validation of portable in-clinic video-based gait analysis for prosthesis users.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.
methodsA systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.
resultsNinety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.
conclusionThis systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.
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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.