Background The six-minute walk (6MW) is a common walking outcome in multiple sclerosis (MS) thought to measure fatigability in addition to overall walking disability. However, direct evidence of 6MW induced gait deterioration is limited by the difficulty of measuring qualitative changes in walking. Objectives This study aims to (1) define and validate a measure of fatigue-related gait deterioration based on data from body-worn sensors; and (2) use this measure to detect gait deterioration induced by the 6MW. Methods Gait deterioration was assessed using the Warp Score, a measure of similarity between gait cycles based on dynamic time warping (DTW). Cycles from later minutes were compared to baseline cycles in 89 subjects with MS and 29 controls. Correlation, corrected (partial) correlation, and linear regression were used to quantify relationships to walking and fatigue outcomes. Results Warp Scores rose between minute 3 and minute 6 in subjects with mild and moderate disability (p < 0.001). Statistically significant correlations (p < 0.001) to the MS walking scale (MSWS-12), modified fatigue impact scale (MFIS) physical subscale, and cerebellar and pyramidal functional system scores (FSS) were observed even after controlling for walking speed. Regression of MSWS-12 scores on Warp Scores and walking speed explained 73.9% of response variance. Correlations to individual MSWS-12 and MFIS items strongly suggest a relationship to fatigability. Conclusion The Warp Score has been validated in MS subjects as an objective measure of fatigue-related gait deterioration. Progressive changes to gait cycles induced by the 6MW often appeared in later minutes, supporting the importance of sustained walking in clinical assessment.

Gait impairment in multiple sclerosis (MS) can result from muscle weakness, physical fatigue, lack of coordination, and other symptoms. Walking speed, as measured by a number of clinician-administered walking tests, is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand and differentiate the physiological significance of four such features with proven value in MS to facilitate adoption by clinical researchers and incorporation in gait monitoring and analysis systems. In addition, this information can be used to select features that might be appropriate in other forms of disability. Two of the four features are computed using the dynamic time warping (DTW) algorithm: The "DTW Score" is based on the usual DTW distance, and the "Warp Score" is based on the warping length. The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value. Finally, the "Causality Index" is based on the phase slope index between inertial signals from different body parts. Relationships between these measures and the aforementioned gait-related symptoms are determined by applying factor analysis to three common, clinical walking outcomes, then correlating the inertial measures as well as walking speed to each extracted factor. Statistically significant differences in correlation coefficients to the three extracted clinical factors support their distinct physiological meaning and suggest they may have complimentary roles in the analysis of MS-related walking disability.

Nighttime agitation, sleep disturbances, and urinary incontinence (UI) occur frequently in individuals with dementia and can add additional burden to family caregivers, although the co-occurrence of these symptoms is not well understood. The purpose of the current study was to determine the feasibility and acceptability of using passive body sensors in community-dwelling individuals with Alzheimer's disease (AD) by family caregivers and the correlates among these distressing symptoms. A single-group, descriptive design with convenience sampling of participants with AD and their family caregivers was undertaken to address the study aims. Results showed that using body sensors was feasible and acceptable and that patterns of nocturnal agitation, sleep, and UI could be determined and were correlated in study participants. Using data from body sensors may be useful to develop and implement targeted, individualized interventions to lessen these distressing symptoms and decrease caregiver burden. Further study in this field is warranted. [Journal of Gerontological Nursing, 44(8), 19-26.].

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Walking ability can be degraded by a number of pathologies, including movement disorders, stroke, and injury. Personal activity tracking devices gather inertial data needed to measure walking quality, but the required algorithmic methods are an active area of study. To detect changes in walking ability, the similarity between a person's current gait cycles and their known baseline gait cycles may be measured on an ongoing basis. This strategy requires a similarity measure robust to variability encountered in an outpatient scenario, including changes in walking surface, walking speed, and sensor orientation. Here we propose rotation, scale, and offset invariant dynamic time warping (RSOI-DTW), a variant of the well-known dynamic time warping (DTW) algorithm, as a generalization of DTW appropriate for threedimensional inertial data. RSOI-DTW is invariant under rotation, scaling, and offset, yet it preserves the salient features of gait cycles required for gait monitoring. To support this claim, gait cycles from 21 subjects walking with four different styles were compared using both DTW and RSOI-DTW. The data show that RSOI-DTW converges quickly and achieves rotation, scale, and offset invariance. Both algorithms distinguish persons and detect abnormal walking, but only RSOI-DTW does so in the presence of sensor rotation. Variations in walking speed pose a challenge for both algorithms, but performance is improved by collecting baseline information at a variety of speeds.

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Tracking human activity and fitness have been the latest trends in the world of wearable technology.Inertial sensors offer a practical and low cost method for objectively monitoring human movements, and particularly have the applicability to monitor free-living subjects. Shoes, smart phones, watches and other wearables equipped with inertial sensors are able to capture human motion in a non-obtrusive way.Similar sensors are placed on several positions on the human body to monitor a range of different movements, including gait, sit-to-stand transfers, postural sway and limb movements. Inertial sensors have provided a simple and reliable approach to collect human kinematics data. The inertial data collected by these sensors contains valuable information related to human movements, which can be used to monitor and assess the health of a person. But the field of wearable technology still struggles to develop a methodology which can derive relevant information, from the raw inertial data, to aid in health assessment.This work presents a framework to extract information from raw inertial data corresponding to human motion. The framework is a sequential learning process coupled with application-specific knowledge.This framework has three stages: data preparation, feature engineering and information extraction. The first stage converts the raw data into usable data, the second stage extracts relevant attributes from the data and the third stage derives information from these attributes to aid health assessment. A number of techniques can be used at each of the stages, and the appropriate ones are chosen by employing application-specific knowledge as the cornerstone.