Detecting changes in postural sway

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

A performance study of a wearable balance assistance device consisting of scissored-pair control moment gyroscopes and a two-axis inclination sensor

Excessive postural sway while standing can lead to falls and injuries. A designed wearable balance assistance device which consists of scissored-pair control moment gyroscopes and a two-axis inclination sensor is introduced to reduce fall risk from excessive sway among the elderly. The prototype has dimensions of H50cm × W44cm × D30cm and weighs 15.03 kg. This study aims to investigate the effects of generated torque of the prototype on human subjects and aims to determine if the two-axis inclination sensor can detect sway amplitude and sway direction during an occurrence of excessive sway. Two healthy male subjects participated in the study. According to the results, the detected body incline angle related to the acquired sway amplitude of COP trajectories with correlation factors of 0.92 and 0.88 for the two subjects. The detected sway angle related to the acquired sway direction of COP trajectories with the correlation factors of 0.99 and 0.98 for the two subjects. The maximum-allowable generated torque of the prototype with an assigned actuating angle varying within ±15.6° from the acquired sway direction of COP trajectories was able to drive the COP of 60-kilogram-weighted healthy subject maintaining balance at posterolateral limits of stability with an average body incline angle of 5.74° to pass his standing secure zone. The results indicate that the prototype has the potential of being a wearable balance assistance device which can reduce fall risk from excessive sway among the elderly; however, some improvements are still required in regards to shape, size, mass, generated torque, and strength.