The purpose of this study was to analyse the force output of handle and pedal as well as the electromyography (EMG) of lower extremity in different cycling postures. Bilateral pedalling asymmetry indices of force and EMG were also determined in this study. Twelve healthy cyclists were recruited for this study and tested for force output and EMG during steady state cycling adopting different pedalling and handle bar postures. The standing posture increased the maximal stepping torque (posture 1: 204.2 ± 47.0 Nm; posture 2: 212.5 ± 46.1 Nm; posture 3: 561.5 ± 143.0 Nm; posture 4: 585.5 ± 139.1 Nm), stepping work (posture 1: 655.2 ± 134.6 Nm; posture 2: 673.2 ± 116.3 Nm; posture 3: 1852.3 ± 394.4 Nm; posture 4: 1911.3 ± 432.9 Nm), and handle force (posture 1: 16.6 ± 3.6 N; posture 2: 16.4 ± 3.6 N; posture 3: 26.5 ± 8.2 N; posture 4: 41.4 ± 11.1 N), as well as muscle activation (posture 1: 13.6-25.1%; posture 2: 13.0-23.9%; posture 3: 23.6-61.8%; posture 4: 22.5-65.8%) in the erector spine, rectus femoris, tibialis anterior, and soleus. However, neither a sitting nor a standing riding posture affected the hamstring. The riding asymmetry was detected between the right and left legs only in sitting conditions. When a cyclist changes posture from sitting to standing, the upper and lower extremities are forced to produce more force output because of the shift in body weight. These findings suggest that cyclists can switch between sitting and standing postures during competition to increase cycling efficiency in different situations. Furthermore, coaches and trainers can modify sitting and standing durations to moderate cycling intensity, without concerning unbalanced muscle development.

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Abstract. Pressure ulcers are a common and devastating condition faced by users of power wheelchairs. However, proper use of power wheelchair tilt and recline functions can alleviate pressure and reduce the risk of ulcer occurrence. In this work, we show that when using data from a sensor instrumented power wheelchair, we are able to predict with an average accuracy of 92% whether a subject will successfully complete a repositioning exercise when prompted. We present two models of compliance prediction. The first, a spectral Hidden Markov Model, uses fast, optimal optimization techniques to train a sequential classifier. The second, a decision tree using information gain, is computationally efficient and produces an output that is easy for clinicians and wheelchair users to understand. These prediction algorithms will be a key component in an intelligent reminding system that will prompt users to complete a repositioning exercise only in contexts in which the user is most likely to comply.