Faculty Bibliography

Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological disorders, so understanding how to develop this approach for one's area of inquiry is vital. We previously proposed an approach that combined wearable inertial measurement units (IMUs) and ML to classify motions made by stroke patients. However, our approach had computational and practical limitations. We address these limitations here in the form of a primer, presenting how to optimize a sensor-ML approach for clinical implementation. First, we demonstrate how to identify the ML algorithm that maximizes classification performance and pragmatic implementation. Second, we demonstrate how to identify the motion capture approach that maximizes classification performance but reduces cost. We used previously collected motion data from chronic stroke patients wearing off-the-shelf IMUs during a rehabilitation-like activity. To identify the optimal ML algorithm, we compared the classification performance, computational complexity, and tuning requirements of four off-the-shelf algorithms. To identify the optimal motion capture approach, we compared the classification performance of various sensor configurations (number and location on the body) and sensor type (IMUs vs. accelerometers). Of the algorithms tested, linear discriminant analysis had the highest classification performance, low computational complexity, and modest tuning requirements. Of the sensor configurations tested, seven sensors on the paretic arm and trunk led to the highest classification performance, and IMUs outperformed accelerometers. Overall, we present a refined sensor-ML approach that maximizes both classification performance and pragmatic implementation. In addition, with this primer, we showcase important considerations for appraising off-the-shelf algorithms and sensors for quantitative motion assessment.

It is often unclear to the clinical investigator whether observational studies should be submitted to a research ethics committee (REC), mostly because, in general, no active or additional interventions are performed. Moreover, obtaining an informed consent under these circumstances may be challenging, either because these are very large epidemiological registries, or the subject may no longer be alive, is too ill to consent, or is impossible to contact after being discharged. Although observational studies do not involve interventions, they entail ethical concerns, including threats such as breaches in confidentiality and autonomy, and respect for basic rights of the research subjects according to the good clinical practices. In this context, in addition to their main function as evaluators from an ethical, methodological, and regulatory point of view, the RECs serve as mediators between the research subjects, looking after their basic rights, and the investigator or institution, safeguarding them from both legal and unethical perils that the investigation could engage, by ensuring that all procedures are performed following the international standards of care for research. The aim of this manuscript is to provide information on each type of study and its risks, along with actions to prevent such risks, and the function of RECs in each type of study.

Objective: To explore the applicability of an ambulatory inertial sensor (G-walk) to characterize gait function during the Timed Up and Go (TUG) Test under three different conditions.
Background(s): In Parkinson's disease (PD), the current lack of both reliable and feasible biomarkers of gait function and mobility limits the objective characterization of motor ability, clinical progression, and responsiveness to treatments. Current assessments of motor function rely on a clinicians' subjective judgement and/or the patient's self-reported questionnaires, which are not sensitive in capturing subtle changes over time and restrict comparability across raters. Ambulatory inertial sensors allow for non-invasive, wireless transmission of accurate quantitative data and therefore, may represent a useful tool in ambulatory settings. Design/Methods: Nineteen (19) PD patients (H&Y <4) and 10 agematched controls (CTRL) were consecutively enrolled to undergo inertial TUG (iTUG) testing under three experimental conditions: normal walking (iTUGnorm), dual task walking (iTUGcog), and at maximum speed (iTUGfast). The time needed to complete each test was sub-divided into six distinct phases quantified by the sensor: sitto- stand (1), forward gait (2), mid-turn (3), return gait (4), end-turn (5) and stand-to-sit (6). Other assessments included UDPRS Part III, MoCA, depression, fatigue, Benton and Rey-Osterrieth visual tests.
Result(s): A total of nineteen PD patients and ten CTRLs completed all assessments. PD patients were divided into mild (H&Y=2, n=12) and moderate (H&Y=3, n=7) disease severity. One-way-ANOVA and correlation analysis were performed. Different patterns of kinematic performance were observed (figure 1.A and 1.B). In PD, iTUG correlations were found with cognitive function, visual performance and motor severity, while in CTRLs there was only a correlation with motor performance only. iTUGfast performance seemed more sensitive experimental condition when PD was stratify by severity (figure 1.B).
Conclusion(s): iTUG assessed by an ambulatory inertial sensor is a quick, sensitive and feasible tool for objective measurements of functional mobility in PD. Utilizing validate tests for mobility and gait under different stress conditions can provide distinct information of gait function and mobility. Future longitudinal studies are warranted to better characterize the sensitivity to disease progression and the potential for monitoring and optimizing therapeutic interventions in this patient population. (Figure Presented)