The ability to perform postural transitions such as sit-to-stand is an accepted metric for functional independence. The number of transitions performed in real-life situations provides clinically useful information for individuals recovering from lower extremity injury or surgery. Performance deficits during these transitions are well correlated to negative outcomes in numerous populations. Thus, continuous monitoring and detection of transitions in individuals outside of the clinical setting may provide important, clinically relevant information regarding the progression of physical impairments. In this paper, we propose a new inertial-sensor based approach to detecting transitions utilizing the wavelet transform. This approach performs robustly in both supervised laboratory settings, and in ambient settings. We evaluate the performance of our algorithm on a data set including 334 in-laboratory and 20 in-home postural transitions from individuals with and without motor impairments.

After surgical interventions such as anterior cruciate ligament reconstruction (ACLr), people exhibit altered gait mechanics due to joint impairments. Persistence of altered mechanics after resolution of impairments may be related to daily reinforcement of maladaptive behavior. Quantifying the contribution of such maladaptive motor strategies requires continuous monitoring of locomotor behaviors in the home setting. In this paper, we investigate an inertial sensor based approach to monitoring ambient activities. We evaluate the relative performance of our predictive algorithm on one control and one individual post-ACL reconstruction.

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