Abstract: Characterization of physical activity of a person by analyzing sensor measurements acquired by IMUS placed on wearable fabrics such as for knees. The system provides detection and/or prediction of physical activities. Activity classification is achieved by pattern analysis methods running on appropriate computing platforms, including, but not limited to mobile phones, mobile devices, tablets, laptops, PCs, servers, fitness tracking devices, or microcontrollers located on a preferred embodiment, and/or like. Classified activities can be used for reporting daily exercises as well as to estimate calorie expenditure, and to serve for personalized fitness monitoring and coaching purposes.

Abstract: A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.

Abstract: A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.