English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Assessment of physical performance is essential to predict the frailty level of older adults. The modified Physical Performance Test (mPPT) clinically assesses the performance of nine activities: <italic>standing balance</italic>, <italic>chair rising up &#x0026; down</italic>, <italic>lifting a book</italic>, <italic>putting on and taking off a jacket</italic>, <italic>picking up a coin</italic>, <italic>turning 360&#x00B0;</italic>, <italic>walking</italic>, <italic>going upstairs</italic>, and <italic>going downstairs</italic>. The activity performing duration is the primary evaluation standard. In this study, wearable devices are leveraged to recognize and predict mPPT items&#x2019; duration automatically. This potentially allows frequent follow up of physical performance, and facilitates more appropriate interventions. Five devices, including accelerometers and gyroscopes, were attached to the waist, wrists and ankles of eight younger adults. The system was experimented within three aspects: machine learning models, sensor placement, and sampling frequencies, to which the non-causal six-stages temporal convolutional network using 6.25 Hz signals from the left wrist and right ankle obtained the optimal performance. The duration prediction error ranged from 0.63&#x00B1;0.29 s (<italic>turning 360&#x00B0;</italic>) to 8.21&#x00B1;16.41 s (<italic>walking</italic>). The results suggest the potential for the proposed system in the automatic recognition and segmentation of mPPT items. Future work includes improving the recognition performance of <italic>lifting a book</italic> and implementing the frailty score prediction.

Can Wearable Devices and Machine Learning Techniques Be Used for Recognizing and Segmenting Modified Physical Performance Test Items?