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

Abstract

BackgroundAssistive technologies aim to increase quality of life, reduce dependence on care giver and on the long term care system. Several studies have demonstrated the effectiveness in the use of assistive technology for environment control and communication systems. The progress of brain-computer interfaces (BCI) research together with exoskeleton enable a person with motor impairment to interact with new elements in the environment. 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.MethodsThis study evaluated the ECI in a simulated scenario using a two screen layout: one with the ECI and the other with a simulated home environment, developed for this purpose. The sensorimotor rhythms and the horizontal oculoversion, acquired through BCI2000, a multipurpose standard BCI platform, were used to online control the ECI after the user training and system calibration. Eight subjects with different neurological diseases and spinal cord injury participated in this study. The subjects performed simulated activities of daily living (ADLs), i.e. actions in the simulated environment as drink, switch on a lamp or raise the bed head, during ten minutes in two different modes, AIDE mode, using a prediction model, to recognize the user intention facilitating the scan, and Manual mode, without a prediction model.ResultsThe 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. The results showed a statistically significant differences with p<0.001. Regarding the steps, i.e the number of abstraction levels crossed in the ECI to perform an ADL, the users performed one step in the 90% of the tasks using the AIDE mode and three steps, at least, were necessary in the Manual mode. The user’s intention prediction was performed through conditional random fields (CRF), with a global accuracy about 87%.ConclusionsThe environment analysis and the identification of the user’s behaviors can be used to predict the user intention opening a new paradigm in the design of the ECIs. Although the developed ECI was tested only in a simulated home environment, it can be easily adapted to a real environment increasing the user independence at home.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

“…g.Tec equipment was used in 54 articles for the EEG method , 33 articles used Emotiv equipment , and 24 articles used Compumedics Neuroscan equipment . Further, 16 articles used Brain Products equipment [191][192][193][194][195][196][197][198][199][200][201][202][203][204][205][206], 13 articles used NeuroSky equipment [207][208][209][210][211][212][213][214][215][216][217][218][219], and Neuroelectrics [220][221][222][223][224][225][226][227] and OpenBCI [228][229][230][231][232][233][234][235] equipment were used in eight articles each. Moreover, seven articles used Biosemi equipment [236][237]…”

A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability

“…An ExG cap, developed by Brain Vision , can be used to perform three different biosignal measurements: (1) EEG acquisition, through eight electrodes, to perform BNCI tasks and allow the user to control the assistive robotic device and interact with the control interface; (2) EOG acquisition, using two electrodes placed on the outer canthus of the eyes, to detect left and right eye movements, to provide the user the opportunity to navigate through the menus of the control interface; (3) EKG acquisition to be combined with the respiration and galvanic skin response (GSR) data in order to estimate the affective state of the user [16].…”

“…The AIDE system also incorporates an activity recognition algorithm to improve the performance of the control interfaces. This algorithm has been evaluated with patients [16]. The experience gained in these studies resulted in two different state machines (Figures 5 and 6).…”

“…The study presented in this paper aimed to determine the degree of usability of the complete system in its main application environment, assistance in activities of daily living. In other experiments carried out throughout the project [11,12,16,17,21], the different elements that compose the robotic system described here were validated, as well as the different user interfaces used (EEG, EOG, EMG) [13][14][15].…”

Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users

“…There are certainly many reasons why the translation of sEMG into clinical application is limited [ 14 ]. In the case of applications for (remote) monitoring of the execution of exercises and activities of daily living, user-friendliness and usability certainly play a central role [ 2 , 11 , 15 , 16 ]. Here, self-adhesive pre-gelled electrodes, which are usually used in surface electromyography, quickly reach to their applicability limits.…”