Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
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 f...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/22/6618 |
| _version_ | 1797547453427220480 |
|---|---|
| author | Lukas Adamowicz F. Isik Karahanoglu Christopher Cicalo Hao Zhang Charmaine Demanuele Mar Santamaria Xuemei Cai Shyamal Patel |
| author_facet | Lukas Adamowicz F. Isik Karahanoglu Christopher Cicalo Hao Zhang Charmaine Demanuele Mar Santamaria Xuemei Cai Shyamal Patel |
| author_sort | Lukas Adamowicz |
| collection | DOAJ |
| description | 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: <inline-formula><math display="inline"><semantics><mrow><mn>0.990</mn></mrow></semantics></math></inline-formula> vs. <inline-formula><math display="inline"><semantics><mrow><mn>0.868</mn></mrow></semantics></math></inline-formula> in healthy adults) and a previously published algorithm (precision: <inline-formula><math display="inline"><semantics><mrow><mn>0.988</mn></mrow></semantics></math></inline-formula> vs. <inline-formula><math display="inline"><semantics><mrow><mn>0.643</mn></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T14:44:21Z |
| format | Article |
| id | doaj.art-e5afa2d68d004279b8a3dcc771bc0cad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:44:21Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e5afa2d68d004279b8a3dcc771bc0cad2023-11-20T21:31:44ZengMDPI AGSensors1424-82202020-11-012022661810.3390/s20226618Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower BackLukas Adamowicz0F. Isik Karahanoglu1Christopher Cicalo2Hao Zhang3Charmaine Demanuele4Mar Santamaria5Xuemei Cai6Shyamal Patel7Pfizer, Inc., Cambridge, MA 02139, USAPfizer, Inc., Cambridge, MA 02139, USAPfizer, Inc., Cambridge, MA 02139, USAPfizer, Inc., Cambridge, MA 02139, USAPfizer, Inc., Cambridge, MA 02139, USAPfizer, Inc., Cambridge, MA 02139, USAPfizer, Inc., Cambridge, MA 02139, USAPfizer, Inc., Cambridge, MA 02139, USAThe 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: <inline-formula><math display="inline"><semantics><mrow><mn>0.990</mn></mrow></semantics></math></inline-formula> vs. <inline-formula><math display="inline"><semantics><mrow><mn>0.868</mn></mrow></semantics></math></inline-formula> in healthy adults) and a previously published algorithm (precision: <inline-formula><math display="inline"><semantics><mrow><mn>0.988</mn></mrow></semantics></math></inline-formula> vs. <inline-formula><math display="inline"><semantics><mrow><mn>0.643</mn></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/1424-8220/20/22/6618accelerometerwearable technologyalgorithmfree-livingsit-to-stand |
| spellingShingle | Lukas Adamowicz F. Isik Karahanoglu Christopher Cicalo Hao Zhang Charmaine Demanuele Mar Santamaria Xuemei Cai Shyamal Patel Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back Sensors accelerometer wearable technology algorithm free-living sit-to-stand |
| title | Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back |
| title_full | Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back |
| title_fullStr | Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back |
| title_full_unstemmed | Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back |
| title_short | Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back |
| title_sort | assessment of sit to stand transfers during daily life using an accelerometer on the lower back |
| topic | accelerometer wearable technology algorithm free-living sit-to-stand |
| url | https://www.mdpi.com/1424-8220/20/22/6618 |
| work_keys_str_mv | AT lukasadamowicz assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback AT fisikkarahanoglu assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback AT christophercicalo assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback AT haozhang assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback AT charmainedemanuele assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback AT marsantamaria assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback AT xuemeicai assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback AT shyamalpatel assessmentofsittostandtransfersduringdailylifeusinganaccelerometeronthelowerback |