The purpose of the study was to assess the accuracy of estimates of step frequency from trunk acceleration data analyzed with commonly used algorithms and time window lengths, at a wide range of gait speeds. Twenty healthy young subjects performed an incremental treadmill protocol from 1 km/h up to 6 km/h, with steps of 1 km/h. Each speed condition was maintained for two minutes. A waist worn accelerometer recorded trunk accelerations, while video analysis provided the correct number of steps taken during each gait speed condition. Accuracy of two commonly used signal analysis methods was examined with several different time windows.
Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
Accurate assessment of rolling resistance is important for wheelchair propulsion analyses. However, the commonly used drag and deceleration tests are reported to underestimate rolling resistance up to 6% due to the (neglected) influence of trunk motion. The first aim of this study was to investigate the accuracy of using trunk and wheelchair kinematics to predict the intra-cyclical load distribution, more particularly front wheel loading, during hand-rim wheelchair propulsion. Secondly, the study compared the accuracy of rolling resistance determined from the predicted load distribution with the accuracy of drag test-based rolling resistance. Twenty-five able-bodied participants performed hand-rim wheelchair propulsion on a large motor-driven treadmill. During the treadmill sessions, front wheel load was assessed with load pins to determine the load distribution between the front and rear wheels. Accordingly, a machine learning model was trained to predict front wheel load from kinematic data. Based on two inertial sensors (attached to the trunk and wheelchair) and the machine learning model, front wheel load was predicted with a mean absolute error (MAE) of 3.8% (or 1.8 kg). Rolling resistance determined from the predicted load distribution (MAE: 0.9%, mean error (ME): 0.1%) was more accurate than drag test-based rolling resistance (MAE: 2.5%, ME: −1.3%).
