CIPHomeCare: A Machine Learning-Based System for Monitoring and Alerting Caregivers of Cognitive Insensitivity to Pain (CIP) Patients

Congenital Insensitivity to Pain (CIP) patients, particularly infants, are vulnerable to self-injury due to their inability to perceive pain, which can lead to severe harm, such as biting their hands. This research introduces "CIPHomeCare," a wearable monitoring solution designed to prevent self-injurious behaviors in CIP patients aged 6 to 24 months. The primary focus of this study is developing and applying machine learning algorithms to classify hand-biting behaviors. Using accelerometer data from the STEVAL-BCN002V1 sensor, which is a motion sensor, several machine learning models—K-Nearest Neighbors (KNN), Random Forest (RF), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Logistic Regression (LR)—were trained to differentiate between normal and harmful behaviors. To address data imbalance due to the infrequency of biting events, oversampling techniques such as SMOTE, Borderline-SMOTE, ADASYN, K-means-SMOTE, and SMOTE-ENN were employed to enhance classification performance. Among the algorithms, KNN achieved the highest accuracy (98%) and a sensitivity of 72%, highlighting its effectiveness in detecting harmful hand motions. The findings suggest that machine learning, in combination with wearable technology, can provide accurate, personalized monitoring and timely intervention for CIP patients, paving the way for broader clinical applications and real-time prevention of self-injury. The real-time processing capability of the system enables immediate alerting of caregivers, allowing for timely intervention to prevent injuries, thus improving their quality of life.