ArticleMedical & biological engineering & computing2017
A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking.
Article in Medical & biological engineering & computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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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
20 citing papers in PubMed.
- The performance of a machine learning model in predicting accelerometer-derived walking speed.Heliyon · 2025Article
- Classification of human walking context using a single-point accelerometer.Scientific reports · 2024Article
- The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Free-Living Gait Cadence Measured by Wearable Accelerometer: A Promising Alternative to Traditional Measures of Mobility for Assessing Fall Risk.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson's disease in Japan.Scientific reports · 2023Article
- Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers.Scientific reports · 2022Article
- Detecting Gait Events from Accelerations Using Reservoir Computing.Sensors (Basel, Switzerland) · 2022Article
- Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.NPJ digital medicine · 2022Article
- Assessment of Physical Activity in Adults Using Wrist Accelerometers.Epidemiologic reviews · 2022Review
- Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach.Sensors (Basel, Switzerland) · 2021Article
- Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model.Frontiers in sports and active living · 2021Article
- Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors.Sensors (Basel, Switzerland) · 2020Article
- Indoor vs. Outdoor Walking: Does It Make Any Difference in Joint Angle Depending on Road Surface?Frontiers in sports and active living · 2020Article
- Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies.Digital biomarkers · 2020Article
- Design a prototype for automated patient diagnosis in wireless sensor networks.Medical & biological engineering & computing · 2019Article
- Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis.Current neurology and neuroscience reports · 2019Review
- Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions.Journal of neuroengineering and rehabilitation · 2019Article
- What is the Best Configuration of Wearable Sensors to Measure Spatiotemporal Gait Parameters in Children with Cerebral Palsy?Sensors (Basel, Switzerland) · 2018Article
- Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In daily life, a person's gait-an important marker for his/her health status-is usually assessed using inertial sensors fixed to lower limbs or trunk. Such sensor locations are not well suited for continuous and long duration measurements. A better location would be the wrist but with the drawback of the presence of perturbative movements independent of walking. The aim of this study was to devise and validate an algorithm able to accurately estimate walking cadence and speed for daily life walking in various environments based on acceleration measured at the wrist. To this end, a cadence likelihood measure was designed, automatically filtering out perturbative movements and amplifying the periodic wrist movement characteristic of walking. Speed was estimated using a piecewise linear model. The algorithm was validated for outdoor walking in various and challenging environments (e.g., trail, uphill, downhill). Cadence and speed were successfully estimated for all conditions. Overall median (interquartile range) relative errors were -0.13% (-1.72 2.04%) for instantaneous cadence and -0.67% (-6.52 6.23%) for instantaneous speed. The performance was comparable to existing algorithms for trunk- or lower limb-fixed sensors. The algorithm's low complexity would also allow a real-time implementation in a watch.
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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.