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Dyadic physiological responses are correlated with the quality of interpersonal processes – for example, the degree of “connectedness” in education and mental health counseling. Pattern recognition algorithms could be applied to such dyadic responses to identify the states of specific dyads, but such pattern recognition has primarily focused on classification. This paper instead uses regression algorithms to estimate three conversation aspects (valence, arousal, balance) from heart rate, skin conductance, respiration, and skin temperature. Data were collected from 35 dyads who engaged in 20 minutes of conversation, divided into 10 two-minute intervals. Each interval was rated with regard to conversation valence, arousal, and balance by an observer. When regression algorithms (support vector machines and Gaussian process regression) were trained on other data from the same dyad, they were able to estimate valence, arousal and balance with lower errors than a simple baseline estimator. However, when algorithms were trained on data from other dyads, errors were not lower than those of the baseline estimator. Overall, results indicate that, as long as training data from the same dyad are available, autonomic nervous system responses can be combined with regression algorithms to estimate multiple dyadic conversation aspects with some accuracy. This has applications in education and mental health counseling, though fundamental issues remain to be addressed before the technology is used in practice. 

Lifting and carrying heavy objects is a major aspect of physically intensive jobs. Wearable sensors have previously been used to classify different ways of picking up an object, but have seen only limited use for automatic classification of load position and weight while a person is walking and carrying an object. In this proof-of-concept study, we thus used wearable inertial and electromyographic sensors for offline classification of different load positions (frontal vs. unilateral vs. bilateral side loads) and weights during gait. Ten participants performed 19 different carrying trials each while wearing the sensors, and data from these trials were used to train and evaluate classification algorithms based on supervised machine learning. The algorithms differentiated between frontal and other loads (side/none) with an accuracy of 100%, between frontal vs. unilateral side load vs. bilateral side load with an accuracy of 96.1%, and between different load asymmetry levels with accuracies of 75–79%. While the study is limited by a lack of electromyographic sensors on the arms and a limited number of load positions/weights, it shows that wearable sensors can differentiate between different load positions and weights during gait with high accuracy. In the future, such approaches could be used to control assistive devices or for long-term worker monitoring in physically demanding occupations.