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Falls are frequent and associated with morbidity in persons with multiple sclerosis (PwMS). Symptoms of MS fluctuate, and standard biannual clinical visits cannot capture these fluctuations. Remote monitoring techniques that leverage wearable sensors have recently emerged as an approach sensitive to disease variability. Previous research has shown that fall risk can be identified from walking data collected by wearable sensors in controlled laboratory conditions however this data may not be generalizable to variable home environments. To investigate fall risk and daily activity performance from remote data, we introduce a new open-source dataset featuring data collected from 38 PwMS, 21 of whom are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from eleven body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients. To demonstrate the utility of these data, we explore the use of free-living walking bouts for characterizing fall risk in PwMS, compare these data to those collected in controlled environments, and examine the impact of bout duration on gait parameters and fall risk estimates. Both gait parameters and fall risk classification performance were found to change with bout duration. Deep learning models outperformed feature-based models using home data; the best performance was observed with all bouts for deep-learning and short bouts for feature-based models when evaluating performance on individual bouts. Overall, short duration free-living walking bouts were found to be the least similar to laboratory walking, longer duration free-living walking bouts provided more significant differences between fallers and non-fallers, and an aggregation of all free-living walking bouts yields the best performance in fall risk classification.

Physical therapists evaluate patients' movement patterns during functional tasks; yet, their ability to interpret these observations consistently and accurately is unclear. Physical therapists would benefit from a clinic-friendly method for accurately quantifying movement patterns during functional tasks. Inertial sensors, which are inexpensive, portable sensors capable of monitoring multiple body segments simultaneously, are a relatively new rehabilitation technology. We sought to validate an inertial sensor system by comparing lower limb and lumbar spine kinematic data collected simultaneously with a commercial inertial sensor system and a motion camera system while 10 subjects performed functional tasks. Mean and peak segment angular displacement data were calculated and compared between systems. Mean angular displacement root mean square error between the systems across all tasks and segments was <5°. Mean differences in peak displacements were generally acceptable (<5°) for the femur, tibia, and pelvis segments for all tasks; however, the inertial system overestimated lumbar flexion compared to the motion camera system. These data suggest that the inertial system is capable of measuring angular displacements within 5° of a system widely accepted for its accuracy. Standardization of sensor placement, better attachment methods, and improvement of inertial sensor algorithms will further increase the accuracy of the system.

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