With the arrival of the era of the 100-year lifespan, there is a growing need to build a society that enables individuals experiencing changes in living functions—particularly children and older adults—to lead healthy, safe, and socially active lives. We describe such a society, which would foster resilience to changes in daily living function, as a “living function-resilient society.” Although issues such as health, safety, and participation are currently treated as matters of individual effort, they invariably lie beyond the capabilities of individuals to manage on their own.

 

To solve these critical social problems, it is necessary to understand human beings not simply as individual subjects but rather in terms of their situation within the holistic context of living systems, which includes the real-life settings where such problems arise. In this regard, an indispensable methodology is the integration of new artifacts into actually functioning living systems. Such artifacts would be designed to make complex situations more manageable, thus contributing to the expansion of these living systems. Some of the research themes being tackled by Living-Centric Design, which seeks to create technological systems that can measure, compute, and design living situations, include the following:

 

Sensorization of Deformable Things: Since the 1990s, concepts such as ubiquitous computing, with its sensor-embedded devices, have been evolving under the new name of “the Internet of Things” (IoT). More recently, advancements in affordable depth cameras and machine learning have paved the way for new sensor technologies that can measure human activities based on the deformation of everyday objects. Today, we are moving beyond traditional embedded sensors as we develop new sensor and IoT technologies tailored for an era of new shape measurement techniques.

 

Technologies Promoting the Non-Ergodic Understanding of Human Beings (Technologies for Observing and Understanding Living Situations): Traditionally, research involving an ergodic understanding of human beings typically involved collecting data from many individuals over short periods in laboratories or specialized institutions, on the assumption that the group average would correspond to the time average. However, this approach often struggles with the detection of subtle changes. With the advancement of IoT, it has become possible to collect longitudinal data on individuals, thus enabling the detection of subtle changes on the part of the same individual. We are making use of the capabilities of IoT to produce more nuanced and individualized insights. We develop technologies for understanding living situations based on this new principle.

 

Behavior-Based Technologies for Understanding Living Situations (Technologies for Observing and Understanding Living Situations): In home-based environments, it is now possible to collect data on environmental shapes and bodily postures. This has enabled a behavior-based approach that relies on posture and activity recognition or else categorizes actions without predefined targets using unsupervised learning. This approach is bringing about significant transformations in our understanding of living situations and the development of technologies for living support. With this new behavior-based approach, we are developing technologies for understanding the living situations of populations such as young infants and older adults with dementia. Unlike the robot module behaviors in inclusive architectures of the 1980s and the psychological behaviors studied in the behavioral economics of the early 2000s, we aim to develop a new approach for understanding and modeling everyday life events based on observable behavioral phenomena.

 

Mathematical Techniques for Dealing with Situation: We are developing technologies that enable the observation of human living situations from the perspective of both social and physical phenomena. This involves utilizing semantic big data derived from text and physical data from sensors to analyze living situations. These technologies make it possible to develop a holistic understanding of living situations that enables their replication and prediction, as well as the identification of those that require intervention. This approach goes beyond traditional multi-dimensional multimedia that relies on text, images, and videos to meet a growing need for methodologies that handle systems where both the environment and the people within it are understood as dynamic and ever-changing elements.

 

Episode Engineering Technologies: It was once the case that paper size limited the amount of information that could be conveyed. For example, there was a time when knowledge transfer had to fit on a single sheet of A4-size paper or on both sides at most. This led to the adoption of abstract expressions that only experts could understand and which even they sometimes found incomprehensible. However, with the advent of digitization, information media have significantly changed. The way we represent knowledge is also evolving. Now, with the availability of extensive databases of detailed case (episode) studies, the ability to conduct searches tailored to specific situations constitutes a new form of knowledge presentation that is useful in sites of practice. This approach also offers potential solutions to the challenge of rendering abstract expressions more tangible and comprehensible.

 

Living Simulation Technologies Centered on Life Layer Analysis: Physical simulation techniques such as finite element analysis have long been widely used in the field of mechanical engineering. However, the effective modeling and simulation of living mechanical systems that include humans have not yet been throughly developed. This is primarily due to the lack of dominant equations. Consequently, research tends to shift toward either the micro or macro levels, where such equations do exist, thereby inadvertently moving away from everyday life. By organically integrating the aforementioned themes of non-ergodic understanding, behavior-based understanding, situational mathematical techniques, and episode engineering, we are making progress with the development of simulations that have the ability to computationally represent and manage everyday living situations.

 

Integrating Technologies into Living Systems for Real-Life Applications: Adopting a robust methodology is important to ensure that social implementation is not oversimplified as an issue of attitude (close interaction). A crucial aspect of functioning in actual living environments is having a thorough understanding of real-life conditions. Our research focuses on integrating technologies like biometrics (e.g., the estimation of core body temperatures), which work under real-life conditions, into behavioral, biomechanical, psychological, and living situation models to create unified living systems.

 

Empowering Reality Technologies (Living Design Technologies): When designing living spaces like homes, daycare centers, and long-term care facilities, the traditional approach of proposing various solutions in line with existing problems is no longer sufficient. It is essential that we collect extensive data not only to understand the actual reasons why these proposals often fail to be adopted in real-life settings but also to develop strategies specifically aimed at overcoming these identified challenges. Our research involves gathering data on these “impossible worlds” and developing information presentation technologies and mechanical systems that will facilitate empowerment and practical support.

  

These research initiatives are being advanced through multidisciplinary collaborations with the Childhood Injury Prevention Engineering Council (CIPEC), the National Institute of Advanced Industrial Science and Technology, the Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, the Japan Sport Council, the Tokyo Fire Department, the Tokyo Metropolitan Government, and the cities of Kawasaki and Atsugi in Kanagawa Prefecture, Chichibu in Saitama Prefecture, and Omura in Nagasaki Prefecture.

 

* “Living Function Resilience” refers to support provided by robots, AI, and social services that enables individuals to maintain safe and healthy social participation even as their physical and cognitive functions change.