Abstract
The rapid rates of industrialization and urbanization have induced considerable changes in family structures, increasing the number of older adults who voluntarily or involuntarily live alone. The rapid increase in the population of older adults living alone has raised many safety concerns, with fall-induced injuries and dementia presenting immediate dangers to older adults. Falls are prevalent in older adults, and not only cause injuries for the individuals, but also impose an extremely heavy burden on family members and caregivers. Furthermore, dementia is common among older adults in aging societies and is usually accompanied by dysfunctions in daily living activities, causing considerable difficulties for family members. The objective of this study was to develop a remote monitoring and control (M&C) smart floor system for detecting falls and wandering patterns in older adults with dementia in order to provide comprehensive care assistance. The proposed system integrates a floor detection sensor and Wi-Fi technology to analyze and determine the occurrence of falls and wandering in older adults with dementia. Conventionally, detection processes for falls and wandering in older adults with dementia are conducted using visual monitoring or wearable detectors, which may reduce the privacy, comfort, and convenience of older adults. By contrast, the proposed system maintains users’ privacy and eliminates the inconvenience associated with wearing detectors. The system determines fall behaviors and wandering patterns in older adults with dementia; when an accident occurs, the system can issue a warning and notify medical care units or relatives for immediate attention, thus reducing the occurrence of further accidents. The remote M&C and warning functions of the proposed system were verified through experiments.
Introduction
The rapid rates of industrialization and urbanization have engendered extensive changes in family structures, resulting in an increasing number of older adults living alone either voluntarily or involuntarily. In Taiwan, the Ministry of the Interior and city and county governments operate a total of 1045 long-term care facilities and nursing homes comprising 42,947 residents, who constitute 1.65% of the older population. Specifically, nearly 98.35% of the older population—or approximately 2.56 million older adults—either lives with their adult children or spouses or lives alone [1]. However, older adults are often still required to manage their daily living activities independently, even if they live with their adult children or spouses. The rapid increase in the population of older adults living alone has raised several safety concerns, with fall-induced injuries as well as dementia constituting the most critical concerns. According to statistics provided by the Ministry of Health and Welfare, falls are the third leading cause of death in older adults after transportation accidents and others [2]. Moreover, the mortality rate associated with falls, to some extent, increases with aging for older adults. Fall-induced injuries not only affect older adults’ physical, mental, and social functions and quality of life but also increase the burden on their caregivers. According to statistics provided by the Department of Nursing and Heath Care, Ministry of Health and Welfare, the number of people with dementia in 2014 was 134,000, and this number is expected to reach 357,000 in 2056. Statistics also revealed that the population of people with dementia is increasing annually [3]. In recent years, the news has frequently reported on older adults with dementia and accidents that have occurred due to wandering. This emphasizes the need for the early diagnosis of dementia to reduce the occurrence of such accidents.
Conventional fall detection devices developed in previous research can primarily be divided into two categories: wearable and nonwearable devices. Wearable devices are usually attached to the user’s body or placed in the user’s carry-on items. Wearable devices use changes in body posture as a basis for judgment by using detection instruments such as an inertial accelerometer equipped with a gyroscope [4–6] or a multipoint radio frequency identification system [7]. The advantages of such devices are small volume, easy application, and low cost. Nevertheless, wearable devices possess several disadvantages; for example, users might forget to carry the devices, which can be inconvenient to wear, and the devices exhibit limitations in some environments (e.g., restrooms and bathrooms). By contrast, fixed devices are fixed or embedded in surrounding environments, such as cameras used for comparing visual information [8]; although such detection methods alleviate the disadvantages of nonwearable devices, users’ privacy remains a major problem to be resolved. In previous research on dementia, common evaluation methods for dementia have mainly involved using assessment scales, including the Mini-Mental State Examination, A lzheimer’s Disease-8 for early-onset dementia, Cognitive Abilities Screening Instrument, and Clinical Dementia Rating Scale, as well as oral questions [9, 10].
Accordingly, the objective of the current study was to design a remote monitoring and control (M&C) smart floor system for detecting falls and wandering patterns in older adults with dementia to assist such older adults who live alone. The system analyzes signals obtained by the floor sensor and then sends notifications when they fall or exhibit wandering patterns. This system incorporates Wi-Fi technology and can detect the most common accidents (i.e., falls and wandering in older adults with dementia). When an accident occurs, the system immediately issues a warning and notifies medical care units and relatives for instant attention, thus reducing the occurrence of further incidents.
System structure and design details
Overall structure of the system
Figure 1 illustrates a block diagram of the proposed system, comprising a front-end sensor, signal processing device, Wi-Fi remote transmitter and receiver, and terminal-end M&C device. The front-end sensor detects falls and wandering patterns in people with dementia; specifically, pressure sensors are installed under a floor mat to detect the occurrence of falls and wandering in older adults who live alone. Regarding the signal processing device, an Arduino module comprising an analog-to-digital (AD) converter and input–output controller processes the received pressure signals; this module is integrated with Wi-Fi technology for wireless transmission. Finally, the terminal-end M&C device is equipped with a Labview human–machine interface to identify accidents and issue warnings to monitoring units, enabling timely responses and thus reducing further accidents.
Overall structure of proposed system.
In the current market, pressure sensors are usually categorized into electrical resistance and electrical capacitance sensors; compared with electrical capacitance sensors, electrical resistance sensors can operate smoothly in humid environments, exhibit higher resistance to heat, and have an average working life reaching approximately 10 million pressure cycles due to its routability. Therefore, this study used electrical resistance pressure sensors manufactured by Interlink Electronics, with each sensor exhibiting a thickness of approximately 0.46 mm and a diameter of 12.7 mm, for experiments. The pressure sensors were attached to the back of a floor mat measuring 35 cm×35 cm; the sensors were attached in a 4×4 matrix configuration for use in the overall experiment (Fig. 2).
Floor mat used for experiments. (a) Pressure sensors attached to the back of floor mat; (b) 4×4 matrix configuration.
The design of the electrical circuit is described as follows. When a study participant steps on such floor mats, the corresponding pressure signals are processed by the Arduino module through AD conversion; if the module determines that the signals indicate fall postures, warnings are raised by activating the light-emitting diode (LED) light and buzzer. The corresponding information is concurrently transmitted through Wi-Fi (Fig. 3).
Electrical circuit diagram.
The aforementioned floor mats with piezoelectric buzzers attached in 4×4 configurations were analyzed. According to the analysis of the signals provided by the sensors, during normal walking and standing, an individual steps on four to six sensors at most; the program settings were thus set to identify these as normal situations and to restart its sensing cycles. However, when participants fell, their bodies mostly covered more than seven sensors; thus, this was set as the threshold for fall occurrence in the program. When pressure is detected on more than seven sensors and the individual is determined to have fallen, red light and buzzer alerts are activated to warn the monitoring staff or the participant’s family members for timely responses. Common fall postures are presented in Fig. 4.
Actual diagram of fall program detection. (a) Top left (b) top middle (c) top right (d) lower left (e) lower middle and (f) lower right.
The fall detection program used the fact that when a participant fell, the participant made contact with the floor mat and exerted pressure on the sensors. The Arduino module then determined whether the corresponding postures reflected fall positions. If the program’s target was reached, the LED light would be activated and a warning issued. In this study, a total of 16 floor mats—designated as ADV0–ADV15—were used; the LED light and buzzer were connected to pin 13, which was set as the output. The initial values for all floor mats were set to 0, and the transmission rate was set to 9600. During testing, when a participant fell, the LED light was activated a warning issued, and the values (i.e., results of the posture detection) were displayed. This information was concurrently transmitted through Wi-Fi to the human–machine interface, which displayed the covered area.
Because patients with dementia demonstrate reduced memory capacity and concentration, they regularly forget their destinations; they exhibit visuospatial cognitive and communication impairments. Therefore, they can only express their physiological anxiety and unwellness through behavior, leading to s pecial wandering patterns. In 2001, Algase [11] categorized patients’ wandering patterns into four types: direct (i.e., linear movement from one location to another), lapping, random, and pacing (Figs. 5 and 6).
Wandering pattern I: linear movement. (a) From bottom to top, (b) from top to bottom, (c) from right to left, and (d) from left to right.
Wandering pattern II. (a) Lapping, (b) random, (c) left–right pacing, and (d) top–down pacing.
During development of the wandering detection program, participants applied pressure on the sensors when they stepped on the floor mats. The pressure signals were processed through AD conversion by the Arduino module; if the received pressure signals reach the program’s target value, the LED light was activated and a warning issued. I n this study, the 16 floor mats (i.e., ADV0–ADV15) were used for wandering detection; the LED light and buzzer were connected to pin 13, which served as the output. The initial values for all floor mats were set to 0, and the transmission rate was set to 9600. When a participant stepped on the floor mats, the program evaluated the corresponding pressure signals and then determined whether the signals reached the program’s target value; if the target was reached, the LED light was activated and a warning issued, and the values (i.e., results of wandering pattern detection) were displayed. The information was concurrently transmitted through Wi-Fi to the human–machine interface, which displayed the participant’s wandering area.
An Arduino Mega 2560 module and an RN171 wireless communication module execute signal processing in the proposed system. The Arduino Mega 2560 module controls the fall detection system. The module comprises digital input–output pins, with 54 pins being used for input and output; 16 pins (i.e., A0–A15) were used as analog inputs. The module has a 10-bit resolution (i.e., 1024 types of values) and an operating voltage of 5 V. The RN171 communication module executes Wi-Fi transmission. This module comprises 14 general-purpose input–output pins, with 4 pins being shared with a universal asynchronous receiver–transmitter unit; the module transmits sensor-derived signals to the information collection and processing center wirelessly through a path of nodes. The wireless transmission testing process is presented in Fig. 7.
Wireless transmission testing process. (a) Wi-Fi module, (b) serial port selection, (c) completed Wi-Fi module setting, and (d) wireless transmission program.
The design of the human–machine interface involves the 4×4 sensor configurations. Specifically, when the steps on the mats reflect falls or wandering patterns in older adults with dementia, the warning light is illuminated and the fall postures and wandering patterns are displayed on the human–machine interface. This enables remote monitoring personnel to provide instant responses and enables medical teams to receive more information immediately. To facilitate the display of received signals on computers, the proposed system applies the Arduino module for signal conversion, which converts analog signals to digital signals using the internal analog to digital converter (ADC) and transmits the signal information to the RN171 module. The human–machine interface is shown in Fig. 8.
Front panel of interface of system for detecting fall postures and wandering patterns in older adults with dementia.
A prototype circuit and sensor floor mats (Fig. 9) were used for the participants to simulate falls and wandering patterns in older adults with dementia. During the experiments, when the Arduino module detected unexpected incidents, the corresponding information was transmitted through Wi-Fi and the human–machine interface displayed whether an accident had occurred.
Sensor floor mat and electrical circuit.
The fall detection experiment was mainly conducted by considering several concerns raised for older adults, such as reduced physical function due to aging, acute and chronic diseases, medicine consumption, and home environment safety, all of which are relevant to the occurrence of falls. In situations where participants had normal walking patterns, the program identified these as normal situations and the LED light and buzzer were not activated (Fig. 10). However, when the participants fell, their bodies contacted larger areas of the floor mats and exerted pressure on the mats accordingly; consequently, the LED light and buzzer were activated to indicate the occurrence of falls; this information was concurrently transmitted through Wi-Fi to the human–machine interface, which displayed the fall postures, contact areas of the postures, and the warning light were displayed (Fig. 11).
Fall detection in situation in which the set target value was not reached. (a) Pressure applied to A0, A1, A4, and A5 by the participant’s feet. (b) Pressure applied to A10, A11, A14, and A15 by the participant’s feet.
Fall detection system in situation in which the set target value was reached. (a) Pressure applied to A0, A1, A4, A5, A8, A9, A12, and A13 by the participant’s body. (b) Occurrence of a fall displayed on the human–machine interface.
Consider, for example, left–right pacing; when a participant wandered, the LED light and buzzer were activated, and the information was transmitted through Wi-Fi to the human–machine interface. The interface displayed the wandering area and the illuminated warning light (Fig. 12).
Wandering pattern (left–right pacing testing). (a) Pressure applied to A8 by the participant’s feet, (b) pressure applied to A9, (c) pressure applied to A10, (d) pressure applied to A11, (e) pressure applied to A10, (f) pressure applied to A9, and (g) pressure applied to A8. (f) Occurrence of wandering displayed on the human–machine interface.
The objective of this study was to design and implement a remote M&C smart floor system for detecting falls and wandering patterns in patients with dementia. Through analyses and detection processes conducted by the sensing floor, this system facilitates the timely provision of assistance to older adults with dementia after the occurrence of falls and wandering. Regarding the system’s hardware, a pressure sensor and an electrical circuit constitute the warning devices, and these are integrated with the smart floor, thus facilitating patient evaluations during normal walking. Concerning the system’s software, the Arduino and Wi-Fi module transmit received information to the terminal-end device, which interprets the information through a LabVIEW-based human–machine interface. In summary, the contributions of this study are outlined as follows: (1) designing and implementing a remote M&C smart floor system for detecting falls and wandering patterns in people with dementia; (2) integrating the smart floor system and Wi-Fi module to achieve real-time evaluation, transmission, and identification of unexpected situations during users’ wandering; and (3) developing a system that can assist family members of older adults, thus enabling them to focus on their work without worry.