ArticleJournal of neuroengineering and rehabilitation2020
Postural transitions detection and characterization in healthy and patient populations using a single waist sensor.
Article 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 15 papers.
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Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
- Sensor Modality Versus Pretraining for Recognising Bed-Exit-Relevant Postural Transitions: A Gyroscope-Focused Feasibility Study on Public Wearable-IMU Data.Sensors (Basel, Switzerland) · 2026Article
- Extensive digital health technology assessment detects subtle motor impairment in mild and asymptomatic Pompe disease.Scientific reports · 2025Article
- Association of real life postural transitions kinematics with fatigue in neurodegenerative and immune diseases.NPJ digital medicine · 2025Article
- Rotational Power: A New Accelerometer-Derived Metric to Assess Functional Impairment in Multiple Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Home-based monitoring of cerebral oxygenation in response to postural changes using near-infrared spectroscopy.GeroScience · 2024Article
- Associations between device-measured physical activity and performance-based physical function outcomes in adults: a systematic review and meta-analysis.BMJ public health · 2023Review
- Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Scoring the Sit-to-Stand Performance of Parkinson's Patients with a Single Wearable Sensor.Sensors (Basel, Switzerland) · 2022Article
- A vision for the future of wearable sensors in spine care and its challenges: narrative review.Journal of spine surgery (Hong Kong) · 2022Review
- Analysis of Relationship between Natural Standing Behavior of Elderly People and a Class of Standing Aids in a Living Space.Sensors (Basel, Switzerland) · 2022Article
- Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.Frontiers in physiology · 2022Article
- A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities.Sensors (Basel, Switzerland) · 2021Article
- Proposed Mobility Assessments with Simultaneous Full-Body Inertial Measurement Units and Optical Motion Capture in Healthy Adults and Neurological Patients for Future Validation Studies: Study Protocol.Sensors (Basel, Switzerland) · 2021Article
- An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults.Sensors (Basel, Switzerland) · 2021Article
- Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson's Disease.Frontiers in aging neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSit-to-stand and stand-to-sit transitions are frequent daily functional tasks indicative of muscle power and balance performance. Monitoring these postural transitions with inertial sensors provides an objective tool to assess mobility in both the laboratory and home environment. While the measurement depends on the sensor location, the clinical and everyday use requires high compliance and subject adherence. The objective of this study was to propose a sit-to-stand and stand-to-sit transition detection algorithm that works independently of the sensor location.
methodsFor a location-independent algorithm, the vertical acceleration of the lower back in the global frame was used to detect the postural transitions in daily activities. The detection performance of the algorithm was validated against video observations. To investigate the effect of the location on the kinematic parameters, these parameters were extracted during a five-time sit-to-stand test and were compared for different locations of the sensor on the trunk and lower back.
resultsThe proposed detection method demonstrates high accuracy in different populations with a mean positive predictive value (and mean sensitivity) of 98% (95%) for healthy individuals and 89% (89%) for participants with diseases.
conclusionsThe sensor location around the waist did not affect the performance of the algorithm in detecting the sit-to-stand and stand-to-sit transitions. However, regarding the accuracy of the kinematic parameters, the sensors located on the sternum and L5 vertebrae demonstrated the highest reliability.
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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.