The recent technological advances in sensor miniaturization and embedded processing have provided new challenges and possibilities to the field of wearable computing. Two research areas are particularly interested by this innovation: healthcare technology applications that are devoted to analyzing the daily activities of a person to evaluate their general health, and personal dead reckoning (PDR) systems that focus on the analysis of the person’s movements to keep track of his/her position in dangerous environments and situations. The identification of suitable algorithms and techniques to process wearable sensors data is a research challenge that must be overcome for both areas. The possibility to compare different solutions over public test benches is crucial to this aim. For this reason, we present the human odometry outdoor dataset (HOOD), a public data set for the PDR systems and the wearable human activity recognition folder (WHARF), a public repository for human activity recognition (HAR), composed of over1,000acceleration recordings referring to14dailyactivities, and a MATLAB library allowing the creation and validation of acceleration models of the activities.