Background/Purpose Advances in sensory technologies provides a method to accurately assess activity levels of people with stroke in their community. This information could be used to determine the effectiveness of rehabilitation interventions as well as provide behavioral enhancing feedback. The purpose of this study was to assess the accuracy of a novel shoe-based sensor system (SmartShoe) to identify different functional postures and steps in people with stroke. The SmartShoe system consists of five force sensitive resistors built into a flexible insole and an accelerometer on the back of the shoe. Pressure and acceleration data are sent via Bluetooth to a smart phone. Methods Participants with stroke wore the SmartShoe while they performed activities of daily living (ADL) in sitting, standing and walking. Data from four participants were used to develop a multi-layer perceptron artificial neural network (ANN) to identify sitting, standing, and walking. A signal-processing algorithm used data from the pressure sensors to estimate number of steps taken while walking. The accuracy, precision and recall of the ANN for identifying the three functional postures were calculated using data from a different set of participants. Agreement between steps identified by SmartShoe and actual steps taken was analyzed using the Bland Altman method. Results The SmartShoe was able to accurately identify sitting, standing and walking. Accuracy, precision and recall were all greater than 95%. The mean difference between steps identified by SmartShoe and actual steps was less than 1 step. Discussion The SmartShoe was able to accurately identify different functional postures using a unique combination of pressure and acceleration data in people with stroke as they performed different ADLs. There was a strong level of agreement between actual steps taken and steps identified using the SmartShoe. Further study is needed to determine if the SmartShoe could be used to provide valid information on activity levels of people with stroke while they go about their daily lives in their home and community.

Regaining the ability to walk after a stroke is a major rehabilitation goal. Rehabilitation strategies that are task oriented and intensive can drive cortical reorganization and increase activity levels in people after a stroke. This paper describes a novel, wearable device for use with such rehabilitation strategies. The device is based on the combination of a smartphone and in-shoe sensors, and is designed to operate in free living conditions. Data collected from the device can be used for automatic recognition of postures and activities, characterization of extremity use and to provide behavioral enhancing feedback to patients recovering from a stroke. The proposed wearable platform's operation was validated in a small scale study involving three healthy individuals. The average accuracy of classification of three postures and activities was over 99%. Based on the results of validation and previously reported results on recognition of postures and activities in stroke patients, it is anticipated that recognition of postures and activities may be performed with high accuracy in free living conditions.

These findings may open new avenues for objective assessment of the impact of prescribed footwear on dynamic balance and spatiotemporal parameters of gait and assess gait adaptation after use of custom foot orthoses.

The ability to provide real time feedback concerning a person's activity level and energy expenditure can be beneficial for improving activity levels of individuals. Examples include biofeedback systems used for body weight and physical activity management and biofeedback systems for rehabilitation of stroke patients. A critical aspect of any such system is being able to accurately classify data in real-time so that active and timely feedback can be provided. In the paper we demonstrate feasibility of real-time recognition of multiple household and athletic activities on a cell phone using the data collected by a wearable sensor system consisting of SmartShoe sensor and a wrist accelerometer. The experimental data were collected for multiple household and athletic activities performed by a healthy individual. The data was used to train two neural networks, one to be used primarily for sedentary individuals and one for more active individuals. Classification of household activities including ascending stairs, descending stairs, doing the dishes, vacuuming, and folding laundry, achieved 89.62% average accuracy. Classification of athletic activities such as jumping jacks, swing dancing, and ice skating, was performed with 93.13% accuracy. As proof of real-time processing on a mobile platform the trained neural network for healthy individuals was timed and required less than 4 ms to perform each feature vector construction and classification.