Although there is evidence on the predictors of adverse health outcomes in older individuals, walking speed has typically been measured in laboratory settings (LWS); LWS may be distinct from individuals’ actual walking speed in their daily lives (DWS). We examined whether DWS differs from LWS among older adults, and its association with physical frailty. Participants were 90 community-dwelling older adults. A five-meter normal (LWS_nor) and maximum (LWS_max) walking speed was measured with a stopwatch. DWS was measured using a global positioning system-related smartphone application for one month during their daily lives. DWS_avr, DWS_max, and DWS_sd were defined as the average, maximum, and standard deviation of walking speed for one month. Participants’ mean DWS_avr and DWS_max were 1.28 m/s and 2.14 m/s, respectively, significantly slower than the mean LWS_nor (1.42 m/s) and LWS_max (2.24 m/s); the intraclass correlation coefficient between DWS and LWS were 0.188 to 0.341. DWS was significantly correlated with grip strength, one-legged stance, and LWS. The area under the receiver operating characteristic curve of DWS_sd concerning pre-frailty was largest among DWSs, at 0.615, while that of LWS_nor was 0.643. The findings suggest that DWS differs from LWS and is associated with physical function and pre-frailty.

The present paper deals with posture generation and its application to the selection of design alternatives using the DhaibaHand computational hand model. The operating postures used to push buttons are estimated for target points uniformly distributed on the button plane through the partial use of motion capture data. Two representative subjects (large and small) are used to examine the button disposition. The preferable area for button location is extracted by evaluating the generated postures in terms of reachability and posture margin.