Abstract
When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity), and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP) approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance.
Highlights
The research on multisensor networks has increased significantly in the last 10 years, defining the Internet of Things (IoT) concept
This significant degradation is due to the important reduction in the amount of data for training the system. This result supports the utility of the adaptation algorithm proposed in this paper as the best solution for developing a user-dependent Human Activity Recognition (HAR) system when there is a small amount of data per user
The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six Hidden Markov Models (HMMs), and the last module for segmenting new activity sequences using these models
Summary
The research on multisensor networks has increased significantly in the last 10 years, defining the Internet of Things (IoT) concept These networks typically include cameras, indoor location systems (ILS), microphones, wearable sensors, etc. Thanks to the increment of sensor neural networks, the number of possible research areas has increased rapidly One of these areas is psycho-motor training where an automatic system senses a psychical activity carried out by a person and provides feedback about the performance. When developing a fully automatic system for evaluating motor activities, one important aspect is to segment and recognize the different activities in order to focus the system analysis on some specific ones This process must be carried out by a Human Activity Recognition (HAR) system.
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