Fall risk assessment in the wild: A critical examination of wearable sensor use in free-living conditions

Abstract

Regula rity measures [g'2] (AP, ML, VT, and vector), per step and per stride Harmonic Ratio {AP, ML, VT) Complexity and stability Range Average step and stride duration RMS Cadence (reported for PD in We iss 2014) Step symmetry Median number of steps for bout (n) Slope of domin ant frequency (psd/Hz) Refin ed composite multiscale e ntropy (RCME)-mean a nd variance Refined multiscale perm utation e ntropy (RMPE)-mean and variance Loca l dynamic stability {AP, ML, VT) Wolf's method Loca l dynamic stability per stride (AP, ML, VT) Wolf's method

Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design

“…The western world spends > 80% of their time indoors often exposed to poor indoor environmental conditions, which can be detrimental to health and wellbeing [ 1 ]. The use of passive sensors for measuring indoor environments and monitoring impact on quality of life has become prevalent in recent years [ 2 – 6 ]. Of particular importance is the need for monitoring systems to track individuals and how they respond to environmental changes [ 7 ].…”

“…While healthcare researchers have proposed deploying monitoring equipment of the individual at scale, this has largely been unfeasible due to cost and complexity [ 12 ]. Moreover, there is a requirement to monitor beyond the individual by capturing insights about the general environment and how the individual performs daily tasks, which may negatively impact underlying pathology [ 6 , 13 ]. Use of low-cost sensor technology could facilitate this methodological shift in patient assessment [ 8 , 12 ] and in particular wearable sensing [ 11 ], but in many cases, the technology still requires a great deal of researcher intervention [ 11 , 14 , 15 ].…”

Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review

“…• Variability (or regularity) is the change or fluctuation in walking from one stride to the next [42], [43]. Multiple metrics claim to measure gait variability, leading to many ambiguous definitions [13], [44]. While gait variability may include the discussion of stride-to-stride fluctuations [42], there are further definitions of variability, such as the change in other spatial parameters (e.g., foot clearance) and temporal parameters (e.g., duration of gait phases) from one gait cycle to the next [45].…”

“…For example, Rispens et al define "movement intensity" as the root mean square of the acceleration [66], [79]- [81]. • Entropy, entropy rate, and Lyapunov exponents may be correlated with gait variability (as well as adaptability) [13], [16], [106], [137].…”

“…Evaluating walking in the clinic, while useful, is limited and may not capture the multiple dimensions of skills in everyday mobility. The recent emergence of wearable technology can capture numerous gait characteristics in various settings (e.g., clinical facilities, community settings, and in the home) [13]. Indeed, the amount of physical activity and human movement data collected from wearables is virtually unlimited; however, much of the data are not analyzed or used in a meaningful manner [14].…”