**4.1 Raising awareness using data scattered across multiple organizations in an integrated manner (issue identification)**

A recent text mining technique allows us to process quantities of data that are too big for humans to process. It has the potential for social function, which can be referred to as awareness (issue identification). Using data on medical treatment costs and situations resulting in injury from the Japan Sport Council, we at AIST are developing a technique that automatically analyzes situations that may result in severe injury. This technique identifies phrases unique to situations resulting in severe injury from free descriptive text, based on the assumption that treatment costs increase with increasing severity of injury. It is called "severity cliff analysis [6]." As shown in **Figure 3**, when similar situations are plotted in descending order of treatment costs, a cliff appears where the treatment costs change sharply. The technique allows us to identify the inflection point of the cliff and analyze what causes the severity of injury to increase.

Using this new technique, we analyzed injuries in school environments. Among injuries that involve running and tripping, the severity of injury is higher for hurdle running than for rope jumping and running on flat ground, because it involves hurdles. We investigated measures to prevent hurdle injury and found a hurdle with a top bar that opens like a double door when struck by the foot, which is in use at high schools in Miyagi Prefecture. Thus, severe injuries can be significantly reduced by taking effective preventive measures like this.

The technique allows us to understand in detail situations that result in severe injury by compiling incident data scattered across multiple organizations into big data for analysis by AI. Consequently, we can develop new prevention measures and associate them with existing measures. With data available only at some organizations (some schools, care facilities, etc.), we cannot know the overall occurrence and extent of severe injuries. As a result, known preventive measures remain isolated, and their widespread introduction is delayed. A new approach to improving situations in real-life settings by identifying problems and connecting them with solutions will be increasingly important in the future.

#### **4.2 Monitoring changes in living function using IoT technology**

Elderly people typically lose cognitive and motor functions with age and experience increasing challenges in daily life. There is a need for IoT sensors to detect changes in the living function of individual elderly people as they occur and to call for appropriate interventions. A recent projection for the next 10 years holds that smart homes will provide a market for sensors to support not only home security but also healthcare and safety in the home.

We developed a sensor to make it possible to measure how fast the elderly can walk and how well they can walk unaided. The sensor is designed to be built into an object used in daily life, in this case a handrail [7]. It collects only relevant data (maintaining privacy) and does not need to be attached and detached. We verified the basic functions of the sensor in the living lab at AIST and then installed it in the home of an 88-year-old woman who lives alone. Our study will verify the efficacy of the sensor through long-term monitoring.

**Figure 4** shows how the sensor works and its installation. The sensor comprises two strain gauges fixed above and below a steel bracket secured to the wall. When the subject puts her hand on the handrail, the downward load is detected by the strain gauges.

We conducted a verification test of the sensor in a real-world setting. We installed several sensors on a handrail in the hallway in the subject's house (**Figure 4**, right). **Figure 5** (left) shows a sequence of images of the subject walking while holding the handrail. We collected data continuously for 24 months and plotted the subject's walking speed by using the installation's positionestimation capability (**Figure 5**, right).

We plotted the monthly median walking speed to reveal any changes in the walking pace from January 2016 to December 2017. As **Figure 6** shows, it changed substantially over the period: it decreased from February to August as physical strength declined, increased again from September to November, but declined again from January to March. The subject told us that she initially lost physical strength but regained it from September, but knee pain caused increasing difficulty in walking from January 2017. In May 2017, she broke her thighbone and was admitted to a hospital. In August 2017, she discharged from the hospital. Our results show that the sensor can detect some problems in daily life, although not the cause.

The walking pace of the elderly decreases with advancing age, along with walking patterns such as stride length, walking pace, and lower limb muscle strength.

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**Figure 4.**

**Figure 3.**

*Handrail-type IoT sensor (left, configuration; right, picture).*

*Living Function-Resilient Society in the Centenarian Era: Living Safety Technology Based…*

*Severity cliff analysis, using big data, to identify factors involved in situations resulting in severe injury.*

Such declines increase the need for in-home support services for people who retain a strong need to remain in their own home. Low-cost monitoring of health and mobility would allow quick identification of risks to safety such as by falls. Such monitoring of individual elderly people would allow the timely implementation of appropriate interventions as a form of precision care or individualized care.

*DOI: http://dx.doi.org/10.5772/intechopen.85422*

Continued advances in AI and IoT will support this.

*Living Function-Resilient Society in the Centenarian Era: Living Safety Technology Based… DOI: http://dx.doi.org/10.5772/intechopen.85422*

Such declines increase the need for in-home support services for people who retain a strong need to remain in their own home. Low-cost monitoring of health and mobility would allow quick identification of risks to safety such as by falls. Such monitoring of individual elderly people would allow the timely implementation of appropriate interventions as a form of precision care or individualized care. Continued advances in AI and IoT will support this.

**Figure 3.**

*Internet of Things (IoT) for Automated and Smart Applications*

by taking effective preventive measures like this.

with solutions will be increasingly important in the future.

but also healthcare and safety in the home.

of the sensor through long-term monitoring.

estimation capability (**Figure 5**, right).

**4.2 Monitoring changes in living function using IoT technology**

Using this new technique, we analyzed injuries in school environments. Among injuries that involve running and tripping, the severity of injury is higher for hurdle running than for rope jumping and running on flat ground, because it involves hurdles. We investigated measures to prevent hurdle injury and found a hurdle with a top bar that opens like a double door when struck by the foot, which is in use at high schools in Miyagi Prefecture. Thus, severe injuries can be significantly reduced

The technique allows us to understand in detail situations that result in severe injury by compiling incident data scattered across multiple organizations into big data for analysis by AI. Consequently, we can develop new prevention measures and associate them with existing measures. With data available only at some organizations (some schools, care facilities, etc.), we cannot know the overall occurrence and extent of severe injuries. As a result, known preventive measures remain isolated, and their widespread introduction is delayed. A new approach to improving situations in real-life settings by identifying problems and connecting them

Elderly people typically lose cognitive and motor functions with age and experi-

We developed a sensor to make it possible to measure how fast the elderly can walk and how well they can walk unaided. The sensor is designed to be built into an object used in daily life, in this case a handrail [7]. It collects only relevant data (maintaining privacy) and does not need to be attached and detached. We verified the basic functions of the sensor in the living lab at AIST and then installed it in the home of an 88-year-old woman who lives alone. Our study will verify the efficacy

**Figure 4** shows how the sensor works and its installation. The sensor comprises two strain gauges fixed above and below a steel bracket secured to the wall. When the subject puts her hand on the handrail, the downward load is detected by the

We conducted a verification test of the sensor in a real-world setting. We installed several sensors on a handrail in the hallway in the subject's house (**Figure 4**, right). **Figure 5** (left) shows a sequence of images of the subject walking while holding the handrail. We collected data continuously for 24 months and plotted the subject's walking speed by using the installation's position-

We plotted the monthly median walking speed to reveal any changes in the walking pace from January 2016 to December 2017. As **Figure 6** shows, it changed substantially over the period: it decreased from February to August as physical strength declined, increased again from September to November, but declined again from January to March. The subject told us that she initially lost physical strength but regained it from September, but knee pain caused increasing difficulty in walking from January 2017. In May 2017, she broke her thighbone and was admitted to a hospital. In August 2017, she discharged from the hospital. Our results show that the

The walking pace of the elderly decreases with advancing age, along with walking patterns such as stride length, walking pace, and lower limb muscle strength.

sensor can detect some problems in daily life, although not the cause.

ence increasing challenges in daily life. There is a need for IoT sensors to detect changes in the living function of individual elderly people as they occur and to call for appropriate interventions. A recent projection for the next 10 years holds that smart homes will provide a market for sensors to support not only home security

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strain gauges.

*Severity cliff analysis, using big data, to identify factors involved in situations resulting in severe injury.*

**Figure 4.** *Handrail-type IoT sensor (left, configuration; right, picture).*

**Figure 5.** *The subject using the handrail (left) and motion data obtained (right).*

**Figure 6.**

*Results of 15-month monitoring of walking pace as a health indicator.*
