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

Abstract

Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five of 15 postural sway features derived from the chest and sacrum were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create individualized distributions of each postural sway feature (ID method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the ID method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the ID method over single-observation analyses.

Fig. 1.

Process overview of individualized distribution (ID) and a 30-second single observation (SO) methods. Data were collected using wearable sensors located on the chest and sacrum during various standing tasks. Features were computed using ID or SO method. The resulting feature displays the value of an example feature computed using the standard SO method on top of the distribution obtained from the ID method.

Fig. 2.

Heat maps illustrating significant correlations between postural sway features derived from the chest sensor (top) and force platform (bottom). Boxes are colored based on correlation strength and direction (positive/negative). Non-significant correlations are shown in white. α= 0.05.

Fig. 3.

Number of significant differences in features between tasks in chest and sacrum sensors for the Single Observation (SO) and the Individualized Distributions (ID) methods. Tasks, eyes-open (EO), eyes-closed (EC), and tandem standing (TS), are expected to elicit significantly different postural sway performance. For the ID method, multiple significant differences within each postural sway feature’s statistics are not included (i.e., significant median and standard deviation differences of jerk count as one significant correlation, not two). α= 0.05.

Fig. 4.

Number of significant correlations between age, EDSS, ABC, MFIS, and MSWS and postural sway features for chest (top) and sacrum (middle) sensor data for the Single Observation (SO - blue) and the Individualized Distributions (ID - purple) methods. Range of correlations to ABC during tandem standing with median indicated by black line (bottom). For ID, multiple significant correlations within each postural sway feature’s statistics are not included. Tasks are indicated as eyes-open (EO), eyes-closed (EC), and tandem standing (TS). α= 0.1.

Fig. 5.

Individualized distributions of Jerk found using the chest sensor data for the tandem standing task with subjects sorted in order of Expanded Disability Severity Score (EDSS) shown on the left and Activities Specific Balance Confidence Score (ABC) shown on the right. Higher EDSS and balance confidence subjects are at the top of the figures. The strongest correlations were 0.54 and 0.47 for EDSS and ABC with the 5^th percentile.