Non-invasive monitoring of Alzheimer's patients through WiFi channel state information

The design of noninvasive systems for monitoring people's activities is becoming of central interest in recent years. Such systems are essential for those affected by diseases that modify their cognitive status and are not collaborative in using wearable or interactive systems (e.g., mobile apps to communicate). This is particularly true regarding neurodegenerative diseases that involve memory loss, cognitive decline, communication difficulties, behavioral changes, loss of independence, and physical complications. In response to the need of healthcare structures and caregivers to monitor this category of people during their in-home daily life, this paper proposes a nonintrusive system capable of detecting whether or not a person is in his/her room and if he/she is lying on the bed. Checking these conditions is of utmost importance, in particular, during the night to support the monitoring activity of caregivers and social-health operators taking care of people with Dementia and Alzheimer's disease. The proposed system exploits WiFi's Channel State Information (CSI) gathered by common access points installed in the room. CSI data are then used to train a Convolutional Neural Network (CNN) and a fine-tuning technique is applied to increase the generalization capabilities of the CNN model on new environments. In our experimental analysis, we trained the CNN model by collecting CSI data in four different rooms, from two subjects performing three distinct activities. Promising results have been achieved (accuracy > 97.5%) in recognizing the target activities.