Designing a mobile bio-feedback device to support learning activities

Companion robots play an important role to accompany humans and provide emotional support, such as reducing human social isolation and loneliness. Based on recognizing human partner's mental states, a companion robot is able to dynamically adjust its behaviors, and make human-robot interaction smoother and natural. Human emotion has been recognized by many modalities like facial expression and voice. Neurophysiological signals have shown promising results in emotion recognition, since it is an innate signal of human brain which cannot be faked. In this paper, emotional state recognition using a neurophysiology method is studied to guide and modulate companion-robot navigation to enhance its social capabilities. Electroencephalogram (EEG), a type of neurophysiological signals, is used to recognize human emotional state, and then feed into a navigation path planning algorithm for controlling a companion robot's routes. Simulation results show that mobile robot presents navigation behaviors modulated by dynamic human emotional states.

Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals

Background: One of the significant problems in human societies that has existed for a long time. Unfortunately, this problem has assumed more serious dimensions in our communities. One of the challenges faced by methamphetamine abusers is a disorder in emotion regulation, social cognition, and recognition of emotional states. Objectives: In this regard, the purpose of this research was to investigate the effect of emotion regulation training on the recognition of emotional states and social cognition in methamphetamine abusers. Patients and Methods: This research is classified as applied in terms of purpose and quasi-experimental in terms of design, using a pre-test and post-test format with a control group. The study sample consisted of 30 methamphetamine abusers who referred to the TC treatment center (TC) of the Baran Institute in Tabriz in 2024. Sampling was done purposefully, and 15 individuals were randomly assigned to the experimental group and 15 to the control group. To collect data, Bakker's Emotional Processing Questionnaire (2007) and Chen and Lin's Social Cognition Questionnaire (2019) were used. A pre-test was administered to both groups, after which the experimental group received an emotion regulation training protocol over 8 sessions. The research hypotheses were tested using the univariate covariance method. Results: The research findings indicated that emotion regulation training had a positive and significant effect on the recognition of emotional states and social cognition in methamphetamine abusers, increasing the levels of these variables (P < 0.05). Conclusions: This study demonstrated that emotion regulation training improved the recognition of emotional states and social cognition in methamphetamine abusers. Therefore, by offering effective strategies in this area, meaningful steps can be taken to reduce the problems faced by drug abusers.

This study assessed the impact of acute exposure to whole-body vibration in skin temperature of the lower limbs in healthy subjects. Twelve healthy volunteers (mean age 24.55 years) were randomly assigned to two groups and were exposed to different whole-body vibration protocols (35 Hz and 40 Hz frequency and 5-6 mm amplitude). Skin temperature was measured before and immediately after exposure. Infrared thermography measurements revealed that skin temperature decreased significantly after exposure to vibration. The results were different for both vibration frequencies. Exposure to 35 Hz vibration produced significant decreases in skin temperature in some of the studied regions of interest and exposure to 40 Hz vibration produced significant decreases in skin temperature in all studied regions of interest.

Introduction. Disability to recognize emotional and mental states from other people’s facial expressions is acknowledged as one of the major developmental-behavioral problems in autistic children. Therefore, this study aimed to investigate the effect of traditional and computer emotional facial training on the recognition of facial emotional states in children with autism and mother-child relation and parental competence in mothers of autistic children. Material and Methods. The present was clinical trial study. The children in the both groups were trained during 10 sessions. In traditional group children training using 40 images related to four situations as happiness, sadness, anger, and fear. The children in the computer group trained with the Let’s Face It software. These questionnaires were completed one day before and one day and one week after performing the intervention. The data were analyzed using SPSS software. Results. Findings showed that emotional state training has improved the identification of emotional states (p = 0.01), mother-child relation (p = 0.001) and parental competence (p = 0.001) in the both groups. There was also a statistically significant difference between the scores of identifying face emotional states (p = 0.01), mother-child relation (p = 0.001) and parental competence (p = 0.001) in the both groups. But training in traditional group was effectiveness than computer grope. Conclusion. Teaching emotional face to children with autism improved the recognition of emotional states by these children, and subsequently improved the mother-child relationship and motherly sense of competence in the mothers of these children.

Emotional inference by recognizing facial expressions in an image or video has been actively conducted for several years, for example, visual emotion recognition and emotional state recognition. The limitations of illumination conditions, variations of head poses, and partial occlusion are the most challenging problems in image processing. Furthermore, recognizing and inferring insignificant objects in the background is complex. These factors degrade performance. We collected evidence from human sensing through a psychological approach rather than a numerical approach, and converted it into a language through fuzzy theory. This can not only be intuitively explained, but it is also possible to evaluate and analyze numerically. The predicted inference accuracy increased by approximately 9.7% compared with the simple average method.

Emotional expressions serve a crucial role in interpersonal communication between people while improving social life. In particular, information security systems for visual surveillance that aim to recognize human emotional facial states are highly relevant today. Facial expressions are among the most effective and straightforward means of nonverbal interaction in systems with a human-machine interface. Despite significant scientific and engineering advances in emotion recognition, there are still several challenges in improving the performance of real-time human-machine systems that might work more effectively. In this work, a method of facial geometric feature representation is proposed to improve the operation of security systems. The method is designed to automatically reflect the facial expressions of human emotions in the form of quantitative characteristics of geometric shapes. It uses software-generated landmarks for constructing specific geometric characteristics of the face, which serve as input for the method. Our method consists in forming seven geometric shapes based on predefined landmarks, with the subsequent quantitative expression of these shapes. It was established within the method that the movement of the landmarks when changing facial expressions directly changed the value of each geometric shape. The method outputs the quantitative features of seven shapes, later used to classify emotional facial states. Finally, our method was validated using hyperplane classification. The results of computational experiments confirmed the effectiveness of the proposed method for identifying changes in a person’s emotional state by facial expressions. In addition, the use of simple mathematical calculations in our method has significantly reduced the computational complexity against analogs.

Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method

Individuals with cochlear implants (CIs) show reduced word and auditory emotion recognition abilities relative to their peers with normal hearing. Modern CI processing strategies are designed to preserve acoustic cues requisite for word recognition rather than those cues required for accessing other signal information (e.g., talker gender or emotional state). While word recognition is undoubtedly important for communication, the inaccessibility of this additional signal information in speech may lead to negative social experiences and outcomes for individuals with hearing loss. This study aimed to evaluate whether the emphasis on word recognition preservation in CI processing has unintended consequences on the perception of other talker information, such as emotional state. Twenty-four young adult listeners with normal hearing listened to sentences and either reported a target word in each sentence (word recognition task) or selected the emotion of the talker (emotion recognition task) from a list of options (Angry, Calm, Happy, and Sad). Sentences were blocked by task type (emotion recognition versus word recognition) and processing condition (unprocessed versus 8-channel noise vocoder) and presented randomly within the block at three signal-to-noise ratios (SNRs) in a background of speech-shaped noise. Confusion matrices showed the number of errors in emotion recognition by listeners. Listeners demonstrated better emotion recognition performance than word recognition performance at the same SNR. Unprocessed speech resulted in higher recognition rates than vocoded stimuli. Recognition performance (for both words and emotions) decreased with worsening SNR. Vocoding speech resulted in a greater negative impact on emotion recognition than it did for word recognition. These data confirm prior work that suggests that in background noise, emotional prosodic information in speech is easier to recognize than word information, even after simulated CI processing. However, emotion recognition may be more negatively impacted by background noise and CI processing than word recognition. Future work could explore CI processing strategies that better encode prosodic information and investigate this effect in individuals with CIs as opposed to vocoded simulation. This study emphasized the need for clinicians to consider not only word recognition but also other aspects of speech that are critical to successful social communication.

The paper attempted the recognition of multiple driverspsila emotional state from physiological signals. The major challenge of the research is due to the severe inter-driver variation such that the features of different emotional state are high correlated, and it is found that simple decorrelation method cannot normalize the features well to achieve acceptable classification accuracy. Hence, in this paper, we propose to apply a latent variable to represent the hidden attribute of individual driver and use statistical training. In addition, we applied temporal constraints for the inference process to improve the recognition accuracy. Experimental results show that the proposed method outperform existing algorithms used for emotional state recognition.

Complex regional pain syndrome (CRPS) type 1 is a disorder of the extremities characterized by pain, edema, limited range of motion, integument changes, and vasomotor instability often after an inciting event. In the pediatric population, CRPS may be misdiagnosed, or missed entirely, as CRPS literature for this patient population is lacking. Twenty-seven pediatric patient medical records with the diagnosis of CRPS type 1 from the institutional and private practices of the principal investigator (E.J.H.) were reviewed for demographics, inciting event, lower-extremity clinical examination, ancillary testing, previous treatments, time to diagnosis, treatment after diagnosis, and time to resolution of symptoms. Females composed 85.2% of the patient population (n = 23) (mean age of females, 11.11 years). An inciting event preceded pain in 74.1% of patients (n = 20). On physical examination, more than 50% of patients were identified as having changes in skin color and temperature, edema to the affected lower extremity, painful or decreased range of motion in affected joints, and intact lower-extremity motor function. The average time to resolution of symptoms was 6.8 weeks for the entire population. Diagnosis of CRPS type 1 should be considered in a preadolescent female complaining of pain out of proportion after an inciting event with a physical examination demonstrating change in skin color, decrease in skin temperature, edema, and painful or diminished range of motion in affected joints. Prompt diagnosis can decrease the time to resolution of symptoms.

The effects of low-intensity near-infra-red laser irradiation (820 nm; 1.5 and 9.0 J cm−2; pulsed at 12 Hz, 73 Hz and 5 kHz) upon peripheral neurophysiology and skin temperature were investigated using antidromic conduction studies in the human median nerve in vivo. Healthy human volunteers (n = 90) were recruited and allocated randomly to either a control group (n=10) or one of eight experimental groups (two radiant exposures, 1.5 J cm−2 and 9.0 J cm−2 at one of three pulse repetition rates, 12 Hz, 73 Hz or 5 kHz, in addition to a placebo group for each radiant exposure;n = 10 all groups). Analysis of variance (ANOVA) demonstrated a significant (p≤0.05) decrease in skin temperature following irradiation at the lowest radiant exposure (1.5 J cm−2) combined with pulse repetition rates of 73 Hz and 5 kHz, with the greatest effect at 73 Hz. These changes in skin temperature were coupled with increases in negative peak latency (NPL); ie changes in NPL were inversely related to changes in skin temperature. However, in contrast to the authors' previous findings using continuous wave (CW) laser irradiation, differences in NPL were not found to be significant. These findings, therefore, provide little evidence of the neuro-physiological effects of low-intensity infra-red irradiation at the dosage levels and pulse repetition rates used here.

Skin temperatures on the chest and in the wrist area are interesting for continuous monitoring because they can be easily instrumented using an elastic belt or wristband which do not hamper movement in sports, for example. An infrared thermograph camera and NTC thermistors were used to take temperature profiles at these sensing points with a resolution of 0.1 degrees Celsius, and colored thermograms were used to analyze and compare the results. The effect of environmental changes on the skin temperature in the wrist area was studied by cooling and heating the fingers in water at 10 degrees Celsius and 40 degrees Celsius, and the effects of a loading situation on the chest area and wrist area were tested by means of a 30 min bicycle ergometer exercise. NTC thermistors were also used to measure wrist and chest temperatures in two environmental tests at minus 10 degrees Celsius and plus 60 degrees Celsius. Cooling of the fingers naturally reduces the skin temperature in the wrist area and heating increases it due to the venous circulation. The area of the radial artery in the wrist seems to be the most stable temperature point, altering by only about 2 degrees Celsius, whereas the temperature change at other points is up to 4 degrees Celsius. The bicycle ergometer exercise caused a decrease in skin temperature on the chest because of sweating. At the same time the skin temperature on the wrist decreased by about 1.5 degrees Celsius after the first 20 minutes and then returned to its previous level. The area of the radial artery in the wrist seems to be an attractive point for continuous temperature monitoring, especially under normal conditions, and also seems to reflect body temperature quite well upon loading and under different environmental conditions.

Objectives:Spinal manual therapy (SMT) is commonly used for treatment of musculoskeletal pain in the neck, upper back, or upper extremity. Some authors report a multi-system effect of SMT, including peripheral alterations in skin conductance and skin temperature, suggesting that SMT may initiate a sympathetic nervous system (SNS) response. The focus of this evidence-based review and meta-analysis is to evaluate the evidence of SNS responses and clinically relevant outcomes following SMT to the cervical or thoracic spine.Methods:A systematic search used the terms: ‘manual therapy’, ‘SMT’, ‘spinal manipulation’, ‘mobilization’, ‘SNS’, ‘autonomic nervous system’, ‘neurophysiology’, ‘hypoalgesia’, ‘pain pathophysiology’, ‘cervical vertebrae’, ‘thoracic vertebrae’, ‘upper extremity’, and ‘neurodynamic test’. Data were extracted and within-group and between-group effect sizes were calculated for outcomes of skin conductance, skin temperature, pain, and upper extremity range of motion (ROM) during upper limb neurodynamic tests (ULNTs).Results:Eleven studies were identified. Statistically significant changes were seen with increased skin conductance, decreased skin temperature, decreased pain, and increased upper extremity ROM during ULNT.Discussion:A mechanical stimulus at the cervical or thoracic spine can produce a SNS excitatory response (increased skin conductance and decreased skin temperature). Findings of reduced pain and increased ROM during ULNT provide support to the clinical relevance of SMT. This evidence points toward additional mechanisms underlying the therapeutic effect of SMT. The effect sizes are small to moderate and no long-term effects post-SMT were collected. Future research is needed to associate peripheral effects with a possible centrally-mediated response to SMT.

Effect of whole-body cryostimulation on skin temperature and thermal comfort in severe obese patients.