Logistic Regression on Data Generated by Wearable IoT for Predicting Changes in Mental Health Scores

Smartwatches and fitness bands are two examples of IoT devices that may be quite helpful in determining a person's mental health. People are progressively adopting them into their daily life, and smartphone apps that measure step count and sleep utilising sensory perceptions and GPS capabilities are being developed. We can gain a full grasp of the indications of poor mental health by analysing the data they generated. We used physical symptoms recorded by commercially available wearable bands to develop a classification model in Logistic Regression to predict whether the patient's mental health is deteriorating. Previous research has discovered a relationship between phq9 levels in patients and physical activity. We observed that IoT devices may play an important role in mental health research, as demonstrated by the paper Digital Signals in Chronic Pain published in Evidation Health. Even without considering emotional factors, step count and activity monitoring had a greater likelihood of predicting mental health than sleep data obtained by fitness bands.