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The coronavirus disease (COVID-19) outbreak requires rapid reshaping of rehabilitation services to include patients recovering from severe COVID-19 with post-intensive care syndromes, which results in physical deconditioning and cognitive impairments, patients with comorbid conditions, and other patients requiring physical therapy during the outbreak with no or limited access to hospital and rehabilitation centers. Considering the access barriers to quality rehabilitation settings and services imposed by social distancing and stay-at-home orders, these patients can be benefited from providing access to affordable and good quality care through home-based rehabilitation. The success of such treatment will depend highly on the intensity of the therapy and effort invested by the patient. Monitoring patients' compliance and designing a home-based rehabilitation that can mentally engage them are the critical elements in home-based therapy's success. Hence, we study the state-of-the-art telerehabilitation frameworks and robotic devices, and comment about a hybrid model that can use existing telerehabilitation framework and home-based robotic devices for treatment and simultaneously assess patient's progress remotely. Second, we comment on the patients' social support and engagement, which is critical for the success of telerehabilitation service. As the therapists are not physically present to guide the patients, we also discuss the adaptability requirement of home-based telerehabilitation. Finally, we suggest that the reformed rehabilitation services should consider both home-based solutions for enhancing the activities of daily living and an on-demand ambulatory rehabilitation unit for extensive training where we can monitor both cognitive and motor performance of the patients remotely.

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This paper proposes a classification technique for daily base activity recognition for human monitoring during physical therapy in home. The proposed method estimates the foot motion using single inertial measurement unit, then segments the motion into steps classify them by template-matching as walking, stairs up or stairs down steps. The results show a high accuracy of activity recognition. Unlike previous works which are limited to activity recognition, the proposed approach is more qualitative by providing similarity index of any activity to its desired template which can be used to assess subjects improvement.

This paper studies an analytical solution for the identification problem of linear systems, where inputs are unknown and only output data are accessible. A linear output-only model (LOM) is developed and employed along with physical constraints in state space to simultaneously identify two subspace models, one of which represents the physical system and the other describes the behavior of the unknown input by reconstructing its history. Inputs are assumed to be an arbitrary combination of harmonic signals with constant or time varying exponential amplitudes with non-overlapping frequency ranges with natural frequencies of the physical system. The identification is first performed by a zero-input eigensystem realization algorithm in time domain that estimates the LOM in a generic form; we call it unfixed form, which contains both physical system model and input model in a coupled configuration. By transforming the LOM into a canonical form and utilizing the physical constraints, an analytical approach is developed to decouple the physical model from the input dynamics. The proposed method of analytical output-only system identification (AOSID) is evaluated through simulation of a set of scenarios to demonstrate its capabilities. To this end, we study the effects of type of sensor models, level of measurement noise, and complexity level of the problem on the estimation error. The accuracy of the identified LOM demonstrates that the AOSID method is capable of simultaneous identification of physical model and unknown inputs in the presence of measurement noise with a considerable accuracy at a modest computational expense. Furthermore, AOSID method demonstrates a considerable robustness against nonlinear inputs and white noise disturbances which challenge the assumptions initially made in the theoretical development of the model. KEYWORDSanalytical output-only system identification (AOSID), linear output-only model (LOM), modal parameters estimation, unknown input reconstruction, zero-input eigensystem realization algorithm (ERA)