An accelerometer-based method for estimating fluidity in the sit-to-walk task

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand.

Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures.

In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts.

In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration.

Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation.

Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022.

The validity of an accelerometer-based method for estimating fluidity in the sit-to-walk task in a community setting

[Purpose] Fluidity in the sit-to-walk task has been quantitatively measured with three-dimensional motion analysis system. The purpose of this study was to determine the validity of an accelerometer-based method for estimating fluidity in community-dwelling elderly individuals. [Subjects and Methods] Seventeen community-dwelling elderly females performed a sit-to-walk task. The motion was recorded by an accelerometer, a three-dimensional motion analysis system and a foot pressure sensor simultaneously. The timings of events determined from the acceleration waveform were compared to the timings determined from the three-dimensional motion analysis data (task onset, maximum trunk inclination) or foot pressure sensor data (first heel strike). Regression analysis was used to estimate the fluidity index from the duration between events. [Results] The characteristics of the acceleration waveform were similar to those previously reported in younger adults. Comparisons of event timings from accelerometer and motion analysis system data indicated no systematic bias. Regression analysis showed that the duration from maximum trunk inclination to the first heel strike was the best predictor of fluidity index. [Conclusion] An accelerometer-based method using the duration between characteristic events may be used to precisely and conveniently assess fluidity in a sit-to-walk task in a community setting.