Harnessing Wearable Devices for Emotional Intelligence: Therapeutic Applications in Digital Health

Abstract

Emotional intelligence strives to bridge the gap between human and machine interactions. The application of such systems varies and is becoming more prominent as healthcare services seek to provide more efficient care by utilizing smart digital health apps. One application in digital health is the incorporation of emotion recognition systems as a tool for therapeutic interventions. To this end, a system is designed to collect and analyze physiological signal data, such as electrodermal activity (EDA) and electrocardiogram (ECG), from smart wearable devices. The data are collected from different subjects of varying ages taking part in a study on emotion induction methods. The obtained signals are processed to identify stimulus trigger instances and classify the different reaction stages, as well as arousal strength, using signal processing and machine learning techniques. The reaction stages are identified using a support vector machine algorithm, while the arousal strength is classified using the ResNet50 network architecture. The findings indicate that the EDA signal effectively identifies the emotional trigger, registering a root mean squared error (RMSE) of 0.9871. The features collected from the ECG signal show efficient emotion detection with 94.19% accuracy. However, arousal strength classification is only able to reach 60.37% accuracy on the given dataset. The proposed system effectively detects emotional reactions and can categorize their arousal strength in response to specific stimuli. Such a system could be integrated into therapeutic settings to monitor patients’ emotional responses during therapy sessions. This real-time feedback can guide therapists in adjusting their strategies or interventions.

Wearable Sensor-Based Residual Multifeature Fusion Shrinkage Networks for Human Activity Recognition

“…Some of the hyperparameter configurations for the three datasets are provided in Table 1. Since the data channel and sampling frequencies of HAR sensors in the three investigated datasets are different, the data dimensions of the three datasets are different, and accordingly, the input sizes for the convolutional networks are distinct, as shown in Table 1, where (9, 128), (3,100), and (113, 60) represent the ("channel", "size of the sliding window") of the corresponding datasets. The number of training iterations for each dataset was set to 100, and both the residual blocks and GRU layers were maintained at a depth of two layers.…”

“…In recent years, motivated by the growing development of the Internet of Things (IoT) and artificial intelligence (AI) technologies, AI-based human activity recognition (HAR) has been regarded as pivotal technology, capturing widespread interest [1]. AI-based HAR would have substantial potential and significance in multiple domains such as human-computer interaction [2,3], health monitoring [4,5], and smart homes [6,7].…”