ArticleSensors (Basel, Switzerland)2020
Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test.Acta neurochirurgica · 2026Article
- Instrumental assessment of Sit-to-Stand in Parkinson's disease: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Smartphone-derived joint angular velocities in sit-to-stand motion provide a spatiotemporal marker for symptomatic knee osteoarthritis.Communications medicine · 2026Article
- Automatic identification of postural transitions using a single inertial measurement unit and dynamic time warping: a pilot study in healthy individuals and people with Parkinson's disease.Frontiers in bioengineering and biotechnology · 2026Article
- Deep Learning Predicts Postoperative Mobility, Activities of Daily Living, and Discharge Destination in Older Adults from Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Validity and reliability of inertial measurement units on gait, static balance and functional mobility performance among community-dwelling older adults: a systematic review and meta-analysis.EFORT open reviews · 2025Article
- The Role of Muscle Strength in the Sit-to-Stand Task in Parkinson's Disease.Parkinson's disease · 2023Article
- Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023Article
- Enhancing spatial navigation skills in mild cognitive impairment patients: a usability study of a new version of ANTaging software.Frontiers in human neuroscience · 2023Article
- How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway.Sensors (Basel, Switzerland) · 2022Article
- Advancing Digital Medicine with Wearables in the Wild.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis.Gait & posture · 2022Article
- SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing.JMIR mHealth and uHealth · 2022Article
- Assessment of Thigh Angular Velocity by an Activity Monitor to Describe Sit-to-Stand Performance.Sensors (Basel, Switzerland) · 2022Article
- Estimating balance, cognitive function, and falls risk using wearable sensors and the sit-to-stand test.Wearable technologies · 2022Article
- The Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms for detecting STS transfers rely on multiple sensing modalities or device locations and have predominantly been used for assessment during the performance of prescribed tasks in a lab setting. A novel wavelet-based algorithm for detecting STS transfers from data recorded using an accelerometer on the lower back is presented herein. The proposed algorithm is independent of device orientation and was validated on data captured in the lab from younger and older healthy adults as well as in people with Parkinson's disease (PwPD). The algorithm was then used for processing data captured in free-living conditions to assess the ability of multiple features extracted from STS transfers to detect age-related group differences and assess the impact of monitoring duration on the reliability of measurements. The results show that performance of the proposed algorithm was comparable or significantly better than that of a commercially available system (precision: 0.990 vs. 0.868 in healthy adults) and a previously published algorithm (precision: 0.988 vs. 0.643 in persons with Parkinson's disease). Moreover, features extracted from STS transfers at home were able to detect age-related group differences at a higher level of significance compared to data captured in the lab during the performance of prescribed tasks. Finally, simulation results showed that a monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features. These results point towards the feasibility of using a single accelerometer on the lower back for detection and assessment of STS transfers during daily life. Future work in different patient populations is needed to evaluate the performance of the proposed algorithm, as well as assess the sensitivity and reliability of the STS features.
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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.