Postural transitions detection and characterization in healthy and patient populations using a single waist sensor

Abstract

Background: Sit-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. Methods: For 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. Results: The 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. Conclusions: The 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.

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

“…Therefore, as stated by Winter [57], except for highspeed running and athletic movements, slower movement analyses (e.g., walking) can be reliably done with minor errors using a 25 Hz video camera. Previous studies with older adults used a sampling frequency of 25 Hz to analyze kinematic data during movement transitions, such as sit-to-stand, stand-to-sit, or sit-to-walk [54][55][56]58,59], which reinforces the validity and reliability of using this frequency for movement analysis. The accelerometer data were acquired with a mobile application, which automatically pre-processes the raw data and measures the stand-up time and total time (Figure 2).…”

“…A smartphone application and a digital video camera acquired the data simultaneously. The latter device was considered the reference criterion [ 53 , 54 , 55 ]. The smartphone model was the Xiaomi Mi A1.…”

“…Although the sampling frequency between devices was different (200 vs. 25 Hz), this was not considered a significant limitation [ 53 ]. In fact, previous studies with older adults comparing the accelerometer data vs. video camera data during postural transitions (e.g., sit-to-stand or gait analysis) used different sampling frequencies between devices [ 54 , 55 , 56 ]. Therefore, according to the scientific literature, when comparing handheld devices (e.g., mobile phones) vs. machines (e.g., video cameras), it is impossible to achieve synchronization or sampling frequency equality [ 53 ].…”

Categorisation of Sit-To-Stand Motion Pattern. Human vs Automated Quantitative Assessments.

“…Previous works [35][36][37][38][39] evaluated the performances of various algorithms developed for the identi cation of Sit-to-Stand and Stand-to-Sit postural transitions using data acquired from inertial sensors. In particular, a recent paper of Atrsaei and colleagues [40] validated the accuracy of a new routine based on a single device against visual assessments on-camera recordings of STS movements, obtaining levels of agreement above 94%, in terms of positive predictive values and sensitivity. As a direct comparison with the present study, the use of inertial sensors is usually preferable since they can be also applied in non-clinical environments [41].…”

Quantitative Comparison of Human and Software Reliability in the Categorization of Sit-to-Stand Motion Pattern

“…Previous works [35]- [39] evaluated the performances of various algorithms developed for the identification of Sit-to-Stand and Stand-to-Sit postural transitions using data acquired from inertial sensors. A recent paper of Atrsaei and colleagues [40] validated the accuracy of a new routine based on a single device against visual assessments on-camera recordings of STS movements, obtaining levels of agreement above 94%, in terms of positive predictive values and sensitivity. As a direct comparison with the present study, the use of inertial sensors is usually preferable since they can be also applied in non-clinical environments [41].…”