A novel affect-sensitive human-robot cooperative framework is presented in this paper. Peripheral physiological indices are measured through wearable biofeedback sensors to detect the affective state of the human. Affect recognition is performed through both quantitative and qualitative analyses. A subsumption control architecture sensitive to the affective state of the human is proposed for a mobile robot. Human-robot cooperation experiments are performed where the robot senses the affective state of the human and responds appropriately. The results presented here validate the proposed framework and demonstrate a new way of achieving implicit communication between a human and a robot.
Robots are expected to be pervasive in the society in
a not too distant future where they will work extensively
as assistants of humans in various activities. With this in
view, a novel affect-sensitive architecture for human-robot cooperation is presented
in this paper where the robot is expected to recognize
human psychological states. As a demonstration, an online heart rate
variability analysis to infer the mental stress of a human
engaged in a task is presented. This technique involves real-time
heart rate monitoring, signal processing using both Fourier Transforrn and
Wavelet Transform, and inferring the stress condition based on the
level of activation of the sympathetic and parasympathetic nervous systems
using fuzzy logic. Results from human subject trials are presented
to validate the presented methodology. This stress detection technique is
expected to be useful in the future human-robot cooperation activities,
where the robot will recognize human stress and respond appropriately.
Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait.
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