Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
| Autor: | Shyamal Patel, Hao Zhang, F. Isik Karahanoglu, Christopher Cicalo, Charmaine Demanuele, Lukas Adamowicz, Xuemei Cai, Mar Santamaria |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
030506 rehabilitation Computer science Health Status Real-time computing Accelerometer lcsh:Chemical technology Biochemistry Article Analytical Chemistry 03 medical and health sciences Wearable Electronic Devices 0302 clinical medicine wearable technology Accelerometry Humans lcsh:TP1-1185 Sensitivity (control systems) Electrical and Electronic Engineering Instrumentation Reliability (statistics) Wearable technology Back algorithm free-living Orientation (computer vision) business.industry Sit to stand Work (physics) Reproducibility of Results Atomic and Molecular Physics and Optics sit-to-stand accelerometer 0305 other medical science business 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Sensors, Vol 20, Iss 6618, p 6618 (2020) Sensors (Basel, Switzerland) Sensors Volume 20 Issue 22 |
| ISSN: | 1424-8220 |
| Popis: | 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&rsquo 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&rsquo 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. |
| Databáze: | OpenAIRE |
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