Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis

Abstract

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.

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis

“…However, based on our previous findings [38], the strength of these relationships is poor compared to those observed on postural sway data collected during daily life (e.g., Range and ABC had a correlation of 0.71 from free living data compared to the 0.36 observed using the ID method). This difference may be related to the well documented issue that laboratory based tests are not able to adequately capture the variability with which people, and particularly PwMS whose symptoms are known to change dramatically from day to day, move in free living environments [33], [42]. Considering the results presented herein and our previous findings, postural sway features computed from chest accelerometer data are valid, which allows for easy sensor placement if deployed in a remote setting.…”

“…PwMS who self-reported to have fallen within the previous six-months were characterized as fallers based on the criteria "consider a fall as an event where you unintentionally came to rest on the ground or a lower level." This study has been previously described in detail and the data are publicly available [33]. Participants were asked to complete various activities of daily living, several PRMs, and a neurologist administered Expanded Disability Status Scale (EDSS) [34].…”

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons with Multiple Sclerosis

“…However, based on our previous findings [38], the strength of these relationships is poor compared to those observed on postural sway data collected during daily life (e.g., Range and ABC had a correlation of 0.71 from free living data compared to the 0.36 observed using the SSD method). This difference may be related to the well documented issue that laboratory based tests are not able to adequately capture the variability with which people, and particularly PwMS whose symptoms are known to change dramatically from day to day, move in free living environments [33], [42]. Considering the results presented herein and our previous findings, postural sway features computed from chest accelerometer data are valid, which allows for easy sensor placement if deployed in a remote setting.…”

Digital Phenotypes of Instability and Fatigue Derived from Daily Standing Transitions in Persons with Multiple Sclerosis

“…A key challenge of remote monitoring is the need to identify meaningful data to analyze amongst high volume datasets. One common technique is to inspect data from standardized tasks, which are either assigned to the patient (e.g., [19]- [21]) or performed naturally during daily life (e.g., [22]- [25]). An example of the latter are daily transitions from sitting to standing (sist) or standing to sitting (stsi).…”