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In this paper we describe a wireless wearable system to monitor gait, based on a customized pair of commercial insoles able to collect ground reaction forces by use of 24 embedded cells for each foot. Each insole was combined with a small form factor, low-power Inertial Measurement Unit (IMU) and enabled to communicate via Bluetooth with a base station. We present here the characterization of the system both in terms of performance and in terms of functionality. The system was tested on a subject to demonstrate the usability and the features extraction during gait; this data allow to recognize walking phase in terms of swing and stance phase, step and stride duration, double support and single support duration, both using the pressure sensors and the IMU.

Daily life activities such as working and shopping may cause people to carry overloaded bags, frequently borne in an incorrect way (e.g. only on one shoulder, asymmetrically worn). When these activities alter the gait, back pain incidents can occur. Critical conditions can be monitored taking advantage from a wearable assistant, extracting contextual information by on-body acceleration signals. By acquiring data on trunk, limb and foot during gait, we are able to detect five walking tasks on loaded conditions: two-straps backpack carried on shoulders, backpack carried with a single strap on right and left shoulder, bag carried with the right and left hand. Seven subjects participated walking at self-selected speed on a treadmill carrying a load between 10-12% of their body weight. Subjects repeated each task for five times over three weeks. We classified the activities for a single user by use of KNN, naive Bayes and SVM classifiers. KNN achieved the best recognition accuracy of 96.7% for day dependent classifier training. The sensors placement, which resulted to be different along consecutive days, affects performance evaluation: a +30 rotation on the coronal plane decreases the accuracy to 76.0%.

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