Morbidity and falls are problematic for older people. Wearable devices are increasingly used to monitor daily activities. However, sensors often require rigid attachment to specific locations and shuffling or quiet standing may be confused with walking. Furthermore, it is unclear whether clinical gait assessments are correlated with how older people usually walk during daily life. Wavelet transformations of accelerometer and barometer data from a pendant device worn inside or outside clothing were used to identify walking (excluding shuffling or standing) by 51 older people (83 ± 4 years) during 25 min of 'free-living' activities. Accuracy was validated against annotated video. Training and testing were separated. Activities were only loosely structured including noisy data preceding pendant wearing. An electronic walkway was used for laboratory comparisons. Walking was classified (accuracy ≥97 %) with low false-positive errors (≤1.9%, κ ≥ 0.90). Median free-living cadence was lower than laboratory-assessed cadence (101 vs. 110 steps/min, p < 0.001) but correlated (r = 0.69). Free-living step time variability was significantly higher and uncorrelated with laboratory-assessed variability unless detrended. Remote gait impairment monitoring using wearable devices is feasible providing new ways to investigate morbidity and falls risk. Laboratory-assessed gait performances are correlated with free-living walks, but likely reflect the individual's 'best' performance.

The new daily-life gait assessments were better than the clinical gait assessments at identifying fall risk in our sample of independent living older people. However, further research is required to validate these findings in other populations or those living in residential aged care. Daily-life gait was not only associated with demographics and physiological capacity, but also general health, executive function and the ability to undertake a variety of activities of daily living without excessive concern about falling. Geriatr Gerontol Int 2017; 17: 2274-2282.

Statistical distributions of gait performances provide a reference for future wearable device development and research into the complex relationships between daily-life walking patterns, morbidity, and falls.