# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 9 Results Discussion  
We deployed a simple binary environmental sensor setting at 19 households of elderly people, to test its application in an elderly care setting. The system enables a well-informed personalized healthcare experience with behavioral pattern extraction, health trajectory tracking, anomaly notifications, and cohort analysis capabilities. The applied behavioral data processing pipeline is depicted in Figure 15.  
Daily activity patterns were extracted using unsupervised learning approaches, based on which the user's health trajectory was adaptively learned and tracked over time. Additionally, we carefully formed a scheme to quantify binary-bed-sensor-based sleep quality scores. Health-related anomalies were detected as collective behavior changes over time. Methods to reduce false notifications are also reported, which is crucial to reduce distraction of caregivers. Outing and visits were extracted not only as physical and social  
Figure 14: Point anomalies (red dots) and long-term changes (red lines) detected by the prediction-based deep learning framework using TCN (**top**); and Isolation Forest (**bottom**) on sleep pattern data.  
Figure 15: Overview of the behavioral data processing pipeline.  
activity indicators, but the detected outings were also used to remove non-health-related 'behavioral' changes.  
Population-wide behavioral patterns can be extracted by grouping daily activity profiles observed in the cohort, resulting in a behavior 'dictionary'. Each user is then represented by a bag of 'words' in the dictionary, based on which user segmentation can be conducted to enable risk group stratification.  
Finally, we observe shared behavior pattern changes across the cohort during the COVID-19 lockdown phase in Northern Italy. Specifically, a large percentage of users prolonged their night sleep duration and increased the number/duration of naps during the day, resulting in a decreased sleep efficiency. Additionally, reduced outing/visiting frequencies were detected across the cohort during the lockdown.  
The proposed system is generic and can be customized to fit various sensor settings, household structures and personalized needs. It relies on unsupervised approaches, thus mitigating the manual labeling workload. Additionally, the passive nature of the sensors minimizes user input and interaction, thus improving adherence and mitigating missing data. Such systems could complement the current healthcare experience, without interfering with patients' daily life or compromising their privacy. Future work includes expanding the system with additional environmental sensors, which could be used to infer other activity types, such as kitchen usage, shower/bathing, motion (with densely deployed PIR sensors), and personalized devices with a user friendly device management platform that enables personalized activity tracking with improved user experience; considering user feedback as partially labeled examples to optimize the accuracy of the system; implementing real-time anomaly detection and data exchange; incorporating medically advised intervention into the care system. A wide deployment of such system could improve elderly patients' quality of life at large, and alleviate the pressure on the healthcare system through reduced hospital re-admission.  
R.H., T.B. and B.M.: Methodology, data analytics, and IBM cloud functionality; D.R.: IT architecture; N.M., G.M., P.C.: Sensor design; F.C., E.M.: Patient recruitment and feedback loop; S.N.: Project administration. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the European Union's Horizon 2020 research and innovation program within the project ACTIVAGE (Grant No. 732679).  
The authors declare no conflict of interest.