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We explore the importance of the dynamics of motion, and how it can be used first to develop and to personalize intelligent systems that can understand human motions, then to analyze motions. We propose a framework that uses not only the kinematics information of movements but also the dynamics and allows to classify, analyze and recognize motions, emotions in a non-verbal context. We use the direct measure of the dynamics when available. If not we propose to compute the dynamics from the kinematics, and use it to understand human motions. Finally, we discuss some developments and concrete applications in the field of motion analysis and give some experimental results using gait and simple choreography.

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Optical motion capture systems, which are used in broad fields of research, are costly; they need large installation space and calibrations. We find difficulty in applying it in typical homes and care centers. Therefore we propose to use low cost contact force measurement systems to develop rehabilitation and healthcare monitoring tools. Here, we propose a novel algorithm for motion recognition using the feature vector from force data solely obtained during a daily exercise program. We recognized 7 types of movement (Radio Exercises) of two candidates (mean age 22, male). The results show that the recognition rate of each motion has high score (mean: 86.9%). The results also confirm that there is a clustering of each movement in personal exercises data, and a similarity of the clustering even for different candidates thus that motion recognition is possible using contact force data.