Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors.

The paper suggests a simple energy saving controller for heating, ventilation, and air-conditioning (HVAC) systems that combines information on occupancy and weather with predictive control to save energy in buildings. The controller uses a pulse width modulation strategy and turns on/off the HVAC system based on the optimal decisions of the model predictive controller. The suggested controller is simple yet optimal (in a certain sense), and therefore suitable for residential and small commercial buildings where the cost of the controller is a key factor. The effectiveness of the proposed scheme is illustrated using simulations, whereas the model of the building thermal dynamics was identified based on data from experiments.

An artificial pancreases (AP) is a device for managing diabetes through automated insulin infusion. The control algorithm is the heart of the AP that computes the insulin infusion based on blood glucose measurements. In this article, we investigate the role of multimedia data to enable the advanced control techniques that could personalize AP in elderly type 2 diabetes patients. The performance is evaluated through in silico studies on a patient simulator wherein the patient model is computed based on the data collected from clinical studies.

We propose TRAINER, a TRustworthy Artificial Intelligence-based iNsulin recommendER for elderly individuals with type 2 diabetes, ensuring reliability and trust in insulin dosage recommendations. TRAINER exemplifies this trustworthiness and addresses such concerns by offering reliable insulin recommendations supported by clinical evidence.