Most human movement research takes place within controlled laboratories where researchers observe participant movement. Because a majority of daily activity is performed without observation, we hypothesized movement within a laboratory would vary when there was a small, large or absence of research group. We also hypothesized that personality type would influence movement during observation. Participants completed a personality questionnaire, then walked in a laboratory during three different conditions: no research group (no researchers), small research group (2 researchers), and large research group (6–10 researchers). Results revealed spatiotemporal parameters were altered between conditions, however personality type did not influence changes in movement. As the number of researchers increased, gait speed, cadence, and stride length increased, and step duration decreased. Gait speed increased by .03 m/s from the no research group to the small research group, by .06 m/s from the no research group to the large research group, and by .03 m/s from the small to large research group (all p values < .001). Understanding how researcher observation modifies movement is important and affects the replicability of results, as well as the interpretation of laboratory-based movement studies to activities of daily living in real world settings.

Freezing-of-Gait (FoG) is a syndrome of Parkinson's disease defined by episodes when patients show a complete inability to take a step or continue with their locomotion. In order to develop closed-loop therapeutic strategies, including deep brain stimulation, a reliable means of detecting freezing episodes is required. By using wearable accelerometers, freezing episodes can be detected with energy-based algorithms when the ratio of the energy in the freeze band (3 to 8 Hz) to that of the locomotion band (0.5 to 3 Hz) is above a patient-specific threshold. However, due to the great variability in patient activity type, walking style, and freezing pattern, this detection method often does not work. Here we describe a new FoG-detection method that captures temporal, spatial, and physiological features and uses a support-vector-machine to classify freezing episodes. Since our method uses more diverse features, it is able to more robustly detect FoG events. We have shown that when the energy-based method fails (e.g., area under the receiver operator curve is ~0.5), our new method performs well (e.g., area under ROC curve is 0.96).

The purpose of this study was to determine the effects of a single session of whole-body vibration training (WBVT) on the physical performance of individuals with knee osteoarthritis (OA) in 3 tests designed to simulate activities of daily living (ADLs). Fifteen individuals with symptomatic knee OA completed the Timed-Up-and-Go Test, step test, 20-m walk test, and visual analog scale (VAS) recordings of knee pain intensity. A main effect was detected for time to complete the step test (F[2,28] = 6.243, p = 0.006, (Equation is included in full-text article.)). Post hoc analyses revealed that the time to complete the step test at 5 minutes after WBVT improved significantly (p = 0.042) from that of the pretest. A moderate correlation (r = 0.465, p = 0.001) was found between the VAS scores and the time to complete the step test across all trials. A main effect was found for time to complete the walk test (F[2,28] = 4.370, p = 0.022, (Equation is included in full-text article.)). Post hoc analyses did not indicate significant improvements from pretest seen at 5 minutes after WBVT (p = 0.110) and 1 hour after WBVT (p = 0.224). The WBVT was well tolerated in nearly all the participants, and we observed that an acute bout of WBVT was effective in improving the ability of individuals with knee OA to perform a step test and 20-m walk test. Our findings suggest that WBVT may be an effective nonpharmacologic modality to treat some knee OA symptoms and improve ADLs.