ABSTRACT
Background: Falls are a major public health problem among older adults since they are a primary cause of injuries, functional decline and mortality. Identifying individuals susceptible to falls enables early intervention and prevention strategies. Currently, wearable sensors have emerged as a promising tool for assessing balance and mobility due to their affordability, compact size, and established efficacy. Therefore, the objective of the present study was to evaluate inertial measurement unit (IMU)-based postural sway metrics during quiet stance with four different bases of support and compare them among elderly individuals who are at risk of falling and those who are not. Methods: A triaxial IMU prototype was developed for evaluating postural sway during quiet stance, with various bases of support. Totally, 103 elderly participants with mean age of 68.5 ± 5.7 years were included. Sway metrics, including the root mean square (RMS) of magnitude, summation of range of signal (Range), summation of sway area (SA) and summation of distance (SD) were employed to detect sway perturbations. Results: All of the sway metrics revealed a significantly increasing magnitude of signal trajectory with a decreasing base of support. When comparing IMU sway metrics between groups of individuals at potential risk and non-risk of falls, statistically significant differences were observed in some variables, including RMS, Range, and SA during semi-tandem stance, and Range and SA during one-leg standing. Conclusions: The findings support earlier studies that demonstrated the objective nature of the IMU in assessing balance and predicting future risk of falls. Limited significant findings in this study may be due to the lower sampling rate of the IMU prototype (50 Hz) compared to commonly reported frequencies (100 Hz), as well as the inclusion of elderly ambulatory participants who were capable of being independent in their daily activities. The IMU is capable of providing comprehensive data, and detecting subtle changes, early signs of balance impairment and fall tendencies.
ABSTRACT
Postural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inertial sensor and apply machine learning (ML) algorithms for predicting trajectory of the center of pressure (COP) path of postural sway. Fifty-three healthy adults, with a mean age of 46 years, participated. The inertial sensor prototype was investigated for its concurrent validity relative to the COP path length obtained from the force platform measurement. Then, ML was applied to predict the COP path by using sensor-sway metrics as the input. The results of the study revealed that all variables from the sensor prototype demonstrated high concurrent validity against the COP path from the force platform measurement (ρ > 0.75; p < 0.001). The agreement between sway metrics, derived from the sensor and ML algorithms, illustrated good to excellent agreement (ICC; 0.89-0.95) between COP paths from the sensor metrics, with respect to the force plate measurement. This study demonstrated that the inertial sensor, in comparison to the standard tool, would be an option for balance assessment since it is of low-cost, conveniently portable, and comparable to the accuracy of standard force platform measurement.
