Smart Living Technology-Geriatric Engineering Phase-II: Broadening Scope to Biomedical-Engineering and Construction: Universal Design, AI and an Overall Health Assessment Score

The purpose of this paper describes the transition from a strictly Geriatric Engineering Centric curriculum to a more inclusive universal Design [3] Smart Living Technology (Figure 2) approach that addressing intelligent living environments Biomedical Engineering Tech: i.e. medical sensors and construction techniques spanning multiple engineering disciplines to facilitate independent living for all age groups. The significance of this paper to integrate the design of intelligent systems into multiple mainstream engineering programs, serving all age groups that enhances the original focus on primarily older individuals. This proactive approach to providing assistive systems can significantly lower heath costs by significantly reducing the need for specialized assistive living environments. The Overall health score provides a snapshot of many health indicators that can expeditiously be addressed and notify individuals and medical practitioners of impending health concerns before they become acute.

The Term Smart Living Technology was chosen since it defines a area of study that describes designing integrated systems to benefit all age group, including elder individuals. [4] “Smart Living Technology is a growing trend that uses technology to improve daily life, increasing efficiency, affordability, and sustainability. At its core, Smart Living Technology aims to apply technological advancements to meet human needs, making life more efficient, productive, and economical. Additionally, it seeks to give humans more control over their environment by harnessing the power of human creativity, while simultaneously preserving the environment for future generations.”

Universities are rapidly expanding their course offerings in interconnected Smart Homes (Table 1). Environmental and health concerns top that list of program. Artificial Intelligence (AI) control and monitoring is essential in developing real time monitoring solutions that are adaptive, i.e. deep learning modules for students are available in MATLAB (Figure 2), adapt to individual lifestyles. They include Health monitoring and cooking solutions that enhance independent living. One of the first was NEST Smart thermostat.

Data from the Environmental Protection Agency (EPA) supports the fact that smart thermostats meeting Energy Star criteria save users an average of 8% on their utility bills [5]. Individual devices, are integrated into entire homes using open-source software such as Home Assistant [6], open-source energy management and monitoring systems. Smart Appliances integration and AI, an evolving science need to be integrated into homes buildings and connected cities [7]. Some examples of smart cities underway, integrated into smart highways, to ease traffic congestion are: Dallas Texas, Chicago Illinois, Denver Colorado, Seattle Washington, Charlotte North Carolina, San Francisco California, Washington DC., Boston Massachusetts, Chattanooga Tennessee, Bolder Colorado,

Table 1: Selection of Smart Home/Living courses and programs in universities.

Universal Design philosophy” Designing physical work and learning environments useable by a wide range of people, regardless of their age, size or disability status” will also benefit over 65 individuals. It incorporates: Equitable use, Flexibility in use, Simple and intuitive use, Perceptible information, Tolerance for error, Low physical effort and Size and space for approach and use.

Applied Solutions

gathering data such as Walking Gate-Analysis in diagnosing disease [8,9]. Gait analysis is a noninvasive process that evaluates how a person walks and stands, including their muscle activity, forces, and energy use. It’s also known as motion analysis. …the patient’s gait can be evaluated in terms of step length, stride length, cadence, cycle time and joint angles. ..Computerized video cameras to show movement in slow motion. Other methods of Gate Analysis: use electrodes on the skin, and pressure sensitive platforms (example: GAITRite-System, GS) that measure foot pressure. (https://www. orthomedctr.com/gait-analysis.php)

Health Score Algorithm: (research in progress)

Smart Living Technology is designed to prepare future engineers / Computer Scientists / MD’s to incorporate and design new technologies into their future systems and medical practices. Several leading software health companies such as APPLE Health and Withing’s are generating an overall health score based on similar input devices used in this paper: Pulse oximeter, BP, ECG and glucose monitors. A system introduced at CES 2025 called the “Omnia Smart Mirror” a hardware/software system provides an overall health score, similar the one we are developing. https:// www.cnet.com/health/ces-2025-the-omnia-smart-mirror-is-asmart- scale-and-health-assessment-in-one/It seems intuitive that all these health monitoring devices will shortly be incorporated into our cell phones, similar to the current Apple Watch, and will in the near future include, in the opening screen of our phones, an overall heath score vastly improving healthcare. Similar to Robin Cooks fictional book “CELL” (referring to your cell phone as your medical practitioner with AI based deep learning and embedded biometric sensors)

Determining an overall health score (Figure 3), a quick synthesis of multiple sensors, is essential in providing a snapshot of health status. Predictive Analytics [10], assist in determining future health outcomes based on previous data and trends potentially can forestall negative outcomes. We postulate this will be useful for all age groups, not only to react but to proactively maintain optimum lifestyles. We relied on Mayo Clinic Studies [11]: Systolic-Diastolic (Systolic/Diastolic: Normal 120/80, Elevated 120-129/<80, Stage 1 Hypertension 130-139/81-89, Stage 2 Hypertension >140/>90), data from the NIH [12]: Normal 95%-100, Contact Physician 91%-94%, Hypoxemia <90% (insufficient oxygen to tissues) and American Diabetes Association [13-18].

The educational challenge is to engage engineering students in designing solutions that benefit at risk individuals including our aging population. Since we can’t anticipate every nuance and variation that face individuals an AI based deep learning system will continually gather data, connected to the ever-evolving smart homes dataset and automatically learn and incorporate lessons learning into new systems. Passive measurement, such as gait analysis, is an example of tools that can predict the onset of more neurogenerative diseases resulting in a decline in cognitive and motor skills. Originally, we focused, in the Geriatric Engineering minor, a small subset of the at-risk population, but the more comprehensive program Smart Living Technology should engage and meet the needs of a wider population.

Besides the authors a number of individuals and countless students have provided input into the okd and new revised program and research. In Particular Al Gittrich 85, a former editor at the NJ start Ledger provided valuable input. William Eisenman BS, MS helped us develop the Economics of Aging course that is part of this new program. Alan Stultz MS, Peter Greene MS, MBA and Bill Tomaskovic MS are on our industrial advisory boards. No external Funding was used to support this study.

The authors have no conflicts of interest to declare.

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