Smartphones are among the most popular wearable devices to monitor human activities. Several existing methods for Human Activity Recognition (HAR) using data from smartphones are based on conventional pattern recognition techniques, but they generate handcrafted feature vectors. This drawback is overcome by deep learning techniques which unfortunately require lots of computing resources, while generating less interpretable feature vectors. The current paper addresses these limitations through the proposal of a Hidden Markov Model (HMM)-based technique for HAR. More formally, the sequential variations of spatial locations within the raw data vectors are initially captured in Markov chains, which are later used for the initialization and the training of HMMs. Meta-data extracted from these models are then saved as the components of the feature vectors. The meta-data are related to the overall time spent by the model observing every symbol for a long time span, irrespective of the state from which this symbol is observed. Classification experiments involving four classification tasks have been carried out on the recently constructed UniMiB SHAR database which contains 17 classes, including 9 types of activities of daily living and 8 types of falls. As a result, the proposed approach has shown best accuracies between 92% and 98.85% for all the classification tasks. This performance is more than 10% better than prior work for 2 out of 4 classification tasks.
The rapid aging of the population combined with the correlation between age and the increase in falls pushes us to create new ways to monitor the elderly. The privacy of radar data can respond to one of the weaknesses of existing technologies, but the huge amount of radar data to process becomes a challenge to process. We therefore introduce a first architecture allowing the processing of its data in real time. The radar technology used is an off-the-shelf Frequency Modulated Continuous Wave radar Ancortek (SDR 980AD2). It is followed by a pre-processing chain composed of Fast Fourier Transform, Filter and Short Time Fourier Transform to obtain time-velocity maps or spectrograms allowing the extraction of features from gait and human activities. An encouraging implementation (using SIMD logic) on Jetson Xavier allows us to move to data stream processing. Continuous monitoring of the subject will save lives, minimize injuries, reduce anxiety and prevent post-fall syndrome (PDS).
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