Design of multi-pad electrotactile system envisioned as a feedback channel for supernumerary robotic limbs.

BACKGROUND: Providing real-time haptic feedback is an important, but still not sufficiently explored aspect of the use of supernumerary robotic limbs (SRLs). We present a multi-pad electrode for conveying multi-modal proprioceptive and sensory information from SRL to the user's thigh and propose a method for stimuli calibration. METHODS: Within two pilot tests, we investigated return electrode configuration and active electrode discrimination in three healthy subjects to select the appropriate electrode pad topology. Based on the obtained results and anthropometric data from the literature, the electrode was designed to have three branches of 10 pads and two additional pads that can be displaced over/under the electrode branches. The electrode was designed to be connected to the stimulator that allows full multiplexing so that specific branches can serve as a common return electrode. To define the procedure for application of this system, the sensation, localization, and discomfort thresholds applicable for the novel electrode were determined and analyzed in 10 subjects. RESULTS: The results showed no overlaps between the three thresholds for individual pads, with significantly different average values, suggesting that the selected electrode positioning and design provide a good active range of useful current amplitude. The results of the subsequent analysis suggested that the stimuli intensity level of 200% of the sensation threshold is the most probable value of the localization threshold. Furthermore, this level ensures a low chance (i.e., 0.7%) of reaching the discomfort. CONCLUSIONS: We believe that envisioned electrotactile system could serve as a high bandwidth feedback channel that can be easily set up to provide proprioceptive and sensory feedback from supernumerary limbs.

Relationship between electrocardiogram-based features and personality traits: Machine learning approach.

BACKGROUND: Based on the known relationship between the human emotion and standard surface electrocardiogram (ECG), we explored the relationship between features extracted from standard ECG recorded during relaxation and seven personality traits (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness, and Disintegration) by using the machine learning (ML) approach which learns from the ECG-based features and predicts the appropriate personality trait by adopting an automated software algorithm. METHODS: A total of 71 healthy university students participated in the study. For quantification of 62 ECG-based parameters (heart rate variability, as well as temporal and amplitude-based parameters) for each ECG record, we used computation procedures together with publicly available data and code. Among 62 parameters, 34 were segregated into separate features according to their diagnostic relevance in clinical practice. To examine the feature influence on personality trait classification and to perform classification, we used random forest ML algorithm. RESULTS: Classification accuracy when clinically relevant ECG features were employed was high for Disintegration (81.3%) and Honesty/humility (75.0%) and moderate to high for Openness (73.3%) and Conscientiousness (70%), while it was low for Agreeableness (56.3%), eXtraversion (47.1%), and Emotionality (43.8%). When all calculated features were used, the classification accuracies were the same or lower, except for the eXtraversion (52.9%). Correlation analysis for selected features is presented. CONCLUSIONS: Results indicate that clinically relevant features might be applicable for personality traits prediction, although no remarkable differences were found among selected groups of parameters. Physiological associations of established relationships should be further explored.