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.

Background: Assessment of center of mass (COM) changes during static stance control has practical implications in clinical settings, notably among older adults. Recently portable and wearable devices, including accelerometers and gyroscopes, have emerged as a promising alternative to traditional clinical and laboratory assessments. The objectives of the study were to evaluate COM postural sway parameters derived from accelerometer and gyroscope data during static balance tasks with varying bases of support in healthy elderly individuals, and to examine the correlation and agreement between accelerometer-based and gyroscope-based parameters in postural sway assessment. Methods: One hundred and fourteen healthy elderly individuals who had not experienced falls within the preceding 6 months and were confirmed to have no risk of falling as determined by the timed up and go test, were included in this study. They were evaluated for postural sway while standing, using the sensor securely with a belt attached to the L5 vertebra. The four-stage balance test, including standing in a double stance (SO), semi-tandem stance (STO), tandem stance (TO), and single-leg stance (SL), was employed to assess each participant’s ability to maintain balance under increasingly challenging standing positions on a stable surface. Results: The study demonstrated that COM posture sway increased with a demanding position and a decreasing base of support. Spearman’s rho correlation coefficients from the anteroposterior and mediolateral planes exhibited strong correlation (0.75 - 0.9). Moderate reliability was observed for both the accelerometer and gyroscope parameters in both planes (intraclass correlation coefficient: 0.5 and 0.75). Conclusions: Accelerometry and gyroscopes provide objective quantification of balance that have the potential to be utilized in conjunction with clinical tests to effectively evaluate the risk of falling.

Background: Elephants in Thailand have changed their roles from working in the logging industry to tourism over the past two decades. In 2020, there were approximately 2700 captive elephants participating in activities such as riding and trekking. During work hours, riding elephants carry one or two people in a saddle on the back with a mahout on the neck several hours a day and over varying terrain. A concern is that this form of riding can cause serious injuries to the musculoskeletal system, although to date there have been no empirical studies to determine the influence of weight carriage on kinematics in elephants. Methods: Eight Asian elephants from a camp in Chiang Mai Province, Thailand, aged between 21 and 41 years with a mean body mass of 3265 ± 140.2 kg, were evaluated under two conditions: walking at a normal speed without a saddle and with a 15% body mass load (saddle and two persons plus additional weights). Gait kinematics, including the maximal angles of fore- and hindlimb joints, were determined using a novel three-dimensional inertial measurement system with wireless sensors. Results: There were no statistical differences between movement angles and a range of motion of the fore- and hindlimbs, when an additional 15% of body mass was added. Conclusion: There is no evidence that carrying a 15% body mass load causes significant changes in elephant gait patterns. Thus, carrying two people in a saddle may have minimal effects on musculoskeletal function. More studies are needed to further test longer durations of riding on different types of terrain to develop appropriate working guidelines for captive elephants. Nevertheless, elephants appear capable of carrying significant amounts of weight on the back without showing signs of physical distress.

Feasibility Study of a Prototype Wearable Inertial Measurement Unit for Elderly Postural Sway Assessment

Gait analysis is a method of gathering quantitative information to assist in determining the cause of abnormal gait for the purpose of making treatment decisions in veterinary medicine. Recent technology has offered the wearable wireless sensor of an inertial measurement unit (IMU) for determining gait parameters. This study proposed the use of a triaxial IMU, comprising an accelerometer, a gyroscope, and a magnetometer, for detecting three-dimensional limb segment motion (XYZ axis) during the gait cycle in Asian elephants. A new algorithm was developed to estimate the kinematic parameter that represents each limb segment of the forelimbs and hindlimbs while walking at a comfortable speed. For future use, this study aimed to create a new prototype of the IMU with a configuration that is tailored to the elephant and apply machine learning in an effort to achieve greater precision.

The early detection of lameness in elephants is essential for effective treatment and welfare enhancement, but subtle gait abnormalities are often difficult to identify visually. This study aimed to evaluate vertical movement symmetry in Asian elephants using cross-correlation analysis of data from inertial measurement units (IMUs). Six elephants were assessed, including individuals with normal gait and one exhibiting an abnormal gait. IMU sensors were attached to the proximal and distal segments of the forelimbs and hindlimbs to record vertical movements during straight-line walking. Cross-correlation coefficients were calculated to quantify the symmetry between the left and right limbs, providing an objective measure of gait patterns. This method provided an objective assessment of gait patterns and demonstrated potential in detecting lameness in elephants. This approach could facilitate the early identification of gait abnormalities, enabling timely interventions and potentially improving treatment outcomes and the welfare of captive elephant populations. Further studies involving a larger number of elephants with confirmed gait abnormalities are necessary to validate the robustness and reliability of this approach.