A new gait phase detection system for continuous monitoring based on wireless sensorized insoles is presented. The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases. The system employs pressure sensors to assess the force exerted by each foot during walking. A fuzzy rule-based inference algorithm is implemented on a smartphone and used to detect each of the gait phases based on the sensor signals. Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc. The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.

M-Health is an emerging area that is transforming how people take part in the control of their wellness condition. This vision is changing traditional health processes by discharging hospitals from the care of people. Important advantages of continuous monitoring can be reached but, in order to transform this vision into a reality, some factors need to be addressed. m-Health applications should be shared by patients and hospital staff to perform proper supervised health monitoring. Furthermore, the uses of smartphones for health purposes should be transformed to achieve the objectives of this vision. In this work, we analyze the m-Health features and lessons learned by the experiences of systems developed by MAmI Research Lab. We have focused on three main aspects: m-interaction, use of frameworks, and physical activity recognition. For the analysis of the previous aspects, we have developed some approaches to: (1) efficiently manage patient medical records for nursing and healthcare environments by introducing the NFC technology; (2) a framework to monitor vital signs, obesity and overweight levels, rehabilitation and frailty aspects by means of accelerometer-enabled smartphones and, finally; (3) a solution to analyze daily gait activity in the elderly, carrying a single inertial wearable close to the first thoracic vertebra.