User activity recognition system to improve the performance of environmental control interfaces: a pilot study with patients

Journal of NeuroEngineering and Rehabilitation, 2019 · DOI: https://doi.org/10.1186/s12984-018-0477-5 · Published: January 10, 2019

Assistive Technology Rehabilitation Biomedical

Simple Explanation

Assistive technologies aim to increase quality of life, reduce dependence on care giver and on the long term care system. This paper aims to evaluate the environment control interface (ECI) developed under the AIDE project conditions, a multimodal interface able to analyze and extract relevant information from the environments as well as from the identification of residual abilities, behaviors, and intentions of the user. The results show that the mean task time spent in the AIDE mode was less than in the Manual, i.e the users were able to perform more tasks in the AIDE mode during the same time.

Study Duration

Not specified

Participants

8 subjects with different neurological diseases and spinal cord injury

Evidence Level

Not specified

Key Findings

1
The mean task time spent in the AIDE mode was less than in the Manual mode, with statistically significant differences (p < 0.001).
2
Users performed one step in the 90% of the tasks using the AIDE mode, while at least three steps were necessary in the Manual mode.
3
The user’s intention prediction was performed through conditional random fields (CRF), with a global accuracy about 87%.

Research Summary

This study evaluated an Environment Control Interface (ECI) in a simulated environment, designed to assist individuals with acquired brain damage or neurodegenerative diseases who require a wheelchair and have limited upper limb mobility. The ECI uses a multimodal system to detect user intention through environmental analysis and behavior identification, based on a conditional random fields (CRF) model. Results indicated that the AIDE mode, which uses a prediction model, allowed users to perform more tasks in less time compared to the manual mode.

Practical Implications

Enhanced User Independence

The ECI, particularly in AIDE mode, shows potential for increasing user independence at home by predicting user intentions and streamlining the interaction with environmental controls.

Improved ECI Design

The study suggests that incorporating environment analysis and user behavior identification can significantly improve the efficiency and usability of ECIs.

Future Real-World Applications

The developed ECI, while tested in a simulated environment, can be adapted to real-world settings, potentially benefiting individuals with motor impairments.

Study Limitations

1
The developed ECI was tested only in a simulated home environment
2
it was the first time that the users handle the complex multimodal control system (EEG+EOG) with this ECI.
3
The CRF model, as it takes into account not only the current state but also the previous states to perform its prediction, it could fail in the prediction of task with common features.

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