ArticlePloS one2016
Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.NPJ digital medicine · 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
- A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses.Annual reviews in control · 2023Article
- Predictivity of daily gait speed using tri-axial accelerometers for two-year incident disability among Japanese older adults.Scientific reports · 2022Article
- Real-world gait speed estimation, frailty and handgrip strength: a cohort-based study.Scientific reports · 2021Observational
- Screening prefrailty in Japanese community-dwelling older adults with daily gait speed and number of steps via tri-axial accelerometers.Scientific reports · 2021Article
- Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings.Sensors (Basel, Switzerland) · 2021Article
- Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach.Sensors (Basel, Switzerland) · 2021Article
- A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor.Computational intelligence and neuroscience · 2021Article
- Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model.Frontiers in sports and active living · 2021Article
- A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method.Sensors (Basel, Switzerland) · 2020Article
- Relationship between Daily and In-laboratory Gait Speed among Healthy Community-dwelling Older Adults.Scientific reports · 2019Article
- Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders.Sensors (Basel, Switzerland) · 2019Article
- Walking-speed estimation using a single inertial measurement unit for the older adults.PloS one · 2019Article
- Physical Activity Comparison Between Body Sides in Hemiparetic Patients Using Wearable Motion Sensors in Free-Living and Therapy: A Case Series.Frontiers in bioengineering and biotechnology · 2018Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Walking speed is widely used to study human health status. Wearable inertial measurement units (IMU) are promising tools for the ambulatory measurement of walking speed. Among wearable inertial sensors, the ones worn on the wrist, such as a watch or band, have relatively higher potential to be easily incorporated into daily lifestyle. Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. A novel kinematic variable is proposed, which finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable (called pca-acc) is obtained by applying sensor fusion on IMU data to find the orientation followed by the use of principal component analysis. An experimental evaluation was performed on 15 healthy young subjects during free walking trials. The experimental results show that the use of the proposed pca-acc variable can significantly improve the walking speed estimation accuracy when compared to the use of raw acceleration information (p<0.01). When Gaussian process regression is used, the resulting walking speed estimation accuracy and precision is about 5.9% and 4.7%, respectively.
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