## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.3 Care Systems
After  
Figure 13: A typical plan of a smart home, including three smart systems, for enhancing the living environment: (i) a system for Parkinson’s disease screening [55] that is visualized in layout number 1 by authors, (ii) a system for gathering complementary data to make a dataset that can manage different health conditions by monitoring [54] that is visualized in layout number 2 by authors, and (iii) a system that can decrease the nightmares of those suffering from PTSD [51] that is visualized in layout number 3 by authors. Note: The location of sensors is schematically shown in the figure and can be relocated. PTSD, post-traumatic stress disorder. that, all the data are recorded and sent to the correlated gateways through Wi-Fi and Bluetooth. Gateways transfer data to the backend system of companies with GPRS, SigFox, or home broadband. A standard JSON data model is used and followed by all the participating companies for the THIM. Thirdly, the data analysis process begins data validation with the data model, and then the data is sorted in the Mongo database. In the next step, machine learning algorithms and multiple data analysis are used to create notifications for healthcare providers. At the fourth stage, \"reminder texts\" and \"flag resources\" are the conducted outputs of data analysis for patients and companies, respectively. The messages in reminder are generated in case of an incomplete or missing measurement and are created with machine learning algorithms and are published to the message queue (MQ). Clinical, environmental, and technical notifications are covered in flags. Finally, the user interface layer or integrated view (iView) collects real-time data every 20 s. Goals achieved in the THIM project are that (1) an algorithm of rule-based reasoning for vital measuring to provide a clinical flag is developed. (2) Technical flags are developed with battery level observation and sensor's connectivity status. (3) Environmental or clinical flags are generated. For example, excessive movements are detected by using environmental sensors and wearable systems. (4) Predictive models, such as urinary tract infection (UTI), are developed by observing the patient's usage of bathroom and temperature of the body. (5) Connecting with caregivers or patients in case of wandering or fall with a fall detector or GPS tracker is provided. (6) Instruction manuals in audio type and reminder for unfinished or missing measurements are provided. (7) As the main parameter, the experiences of patients, caregivers, clinicians, and industrial partner's experts are integrated. [PERSON], [PERSON], [PERSON] and [PERSON] [57] implemented smart home for the next generation by using diverse data, such as security, healthcare, and energy consumption, collected from different sensors and house appliances for management, security, and visualization purposes. The proposed system takes advantage of the following components: home area network (HAN); a user interface to show the trading data with secure access to the different services; a notification system to inform people, along with an android application to communicate via Bluetooth low energy (BLE); ZigBee protocol; smart meters. In the application layer, the data exchange tool is encrypted with AES128 for integrity and security purposes, and BLE is used to communicate with a gateway to either read, write, and enable notification receiving for the energy service part; Node.js server (as a smart gateway); EMS to reckon control schemes based on measured data collected from sensors and smart meters; Representational State Transfer - Application Interface (REST-API) and several stacks for communication to interact with smart devices in the gateway.