With the development of urbanization, the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern. Aging leads to gradual degeneration of the central nervous system, shrinkage of brain tissue, and decline in physical function in many elderlies, making them susceptible to neurological diseases such as Alzheimer’s disease (AD), stroke, Parkinson’s and major depressive disorder (MDD). Due to the influence of these neurological diseases, the elderly have troubles such as memory loss, inability to move, falling, and getting lost, which seriously affect their quality of life. Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time. Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device, device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases. This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems, as well as the techniques, technologies and methods they employ, from the perspective of the needs of the elderly with neurological conditions. Moreover, evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed. Finally, the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed. This review has provided comprehensive and effective tracking and positioning techniques, technologies and methods for the elderly, by which we can obtain the location information of the elderly in real-time and make home-based care more comfortable and safer for the elderly with neurological diseases.
Worldwide, populations are aging at an alarming rate [
At present, home-based care for the older is still the main way of providing for the aged in the world, and most elderly like to enjoy their old age at home, which has derived the “demands for home-based care of the aged”. With the accelerated pace of life and the process of globalization, many young people leave their parents to work and live elsewhere, resulting in aging parents living alone. The first research report on the development of senior care real estate in China released by the China Academy of Urban Economics pointed out that the number of empty-nest families in the urban elderly has reached 52.3%, meanwhile, the number of empty-nest families in the rural elderly has reached 49.8% [
With the popularization of Internet of things technology, the IoT device capabilities, architectures, and protocols are getting more popular, which provides an in-depth overview of the potential healthcare applications [
This review conducts a wide range of in-depth investigations of device-free indoor positioning technology. Evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological diseases are proposed. The main contributions of this paper are summarized as follows:
From the perspective of the needs of the elderly, the main characteristics of the current home care positioning system, as well as the techniques, technologies, and methods are deeply analyzed. We can recommend the best methods and technologies according to the actual situation of home-based care, which provides more mature technical support for solving the problem of home-based care caused by population aging. Considering that wearing positioning devices increases the mobility burden of the elderly with neurological diseases, we choose the device-free positioning technology and systematically introduce it. The device-free positioning technology proposed a safer and more feasible solution for home-based care. The evaluation criteria and feasible solutions of home-based care indoor positioning technology and its development opportunities and challenges in the 6G mobile communication network are discussed. This provides more systematic theoretical support for home-based care.
In a word, the main contribution of this paper lies in the evaluation index of indoor positioning for the elderly with mobility difficulties and the development prospect of the application of device-free positioning to the elderly. Different from previous work, this paper systematically analyzes the application and market of device-free positioning home-based care for the elderly and the development of 6G in the future.
The overall workflow of the reviewed systems is in
This section mainly introduces the concepts and methods of device-free positioning, which are generally divided into active positioning and passive positioning, as shown in
Active positioning means that the target should be equipped with a positioning device before positioning, and then the target device should be positioned.
Active positioning has a wide range of applications in daily life, such as animal trajectory tracking, autonomous driving, and many other applications. Regarding active positioning, the current mainstream research directions include image positioning [
Passive positioning, because the positioning target does not need to carry a positioning device, is also called device-free positioning (DFL) [
Due to the shortcomings of traditional mobile positioning algorithms, Tian et al. proposed an improved mobile anchor node positioning algorithm-SCAN_ET [
The choice of reference nodes has a significant impact on the accuracy of position. The position of the target is determined by measuring the distance between the positioning target and the reference node [
In response to these problems, Nomura et al. proposed a method based on hierarchical clustering [
Proximity detection is also an important research direction in indoor positioning. Although the existing technology cannot meet the requirements of high accuracy, low cost, and high practicability, considering the special nature of home-based care, it can still be applied to the elderly to send alarm information across a specific area. The iBeacon technology of Apple has become mainstream in this field and has almost become the industry standard based on proximity detection service, although its detection accuracy is poor.
To address the problem of accuracy in current proximity detection methods, Zafari et al. proposed two server-based algorithms [
Due to the complexity and uncertainty of the indoor positioning environment, it is difficult to obtain the accurate position of the target. The development of inertial navigation technology and wireless sensor device provides a new research direction for it [
Li et al. [
With the development of mobile communication technologies, due to the complexity of the indoor environment, there are many positioning methods to apply the positioning of unknown nodes in the indoor environment and there are great differences in positioning techniques, technologies, and methods. This section will discuss the positioning techniques, technologies, and positioning methods for the position of the elderly.
There are many methods for indoor positioning [
The trilateration measuring positioning is also called multi-point positioning. The positioning steps on the two-dimensional plane are as follows. Firstly, we take three reference points, taking each reference point as the center, and using the distance of the target nodes as the radius to draw the circle. The target node must be on the ground. Secondly, the position of the intersection area of the three circles on the plane is the target node position. Finally, the minimum multiplication method is used to reduce the positioning error of the unknown node. The positioning model is shown in
Therefore, at least three fixed anchor nodes are taken in the actual application, and as the number of fixed anchor nodes increases, the accuracy and reliability of the system will also increase [
After getting the angle of the received signal the azimuth and pitch angles between the target node and the reference node are obtained by the AOA technique, which are determined based on the direction of the main axis of the receiving node. This method can estimate the location information of the target node based on the angle
In the positioning method based on matching, in this propagation model positioning method, the signal-based intensity is used for ranging [
Among them,
Indoor communication channels can be divided into two categories, divided into line-of-sight (LOS) and non-line-of-sight (NLOS) [
Single sloping model. A simple communication model of dissemination loss and distance between the dissemination loss and distance, which is very easy to use and implement. The loss function is given by
Dual sloping model. Similar to the propagation model of a single sloping model (the difference is to calculate the path of the dissemination loss), it is divided into two parts, as shown in
Among them,
Its signal calculation method is to regard the signal strength characteristics as “fingerprints”, arrange N nodes in advance, measure the signal strength of N reference nodes, get the N-dimensional vector, and then measure the characteristics of each position in the region. The vector “fingerprint” compares the characteristic vector and feature vector database measured by the target to find out the most similar fingerprint corresponding position. The disadvantage is that it cannot correspond to dynamic changes, but the use signal-based dynamic positioning methods of Land Marc can solve this problem. In addition, fingerprint recognition is a time-consuming and labor-intensive process, and because it depends on the predefined map, any change in the environment needs to be re-mapped, so a series of mapping problems are generated. Here are several latest methods to solve this problem.
Researchers introduce a no-map method to solve the mapping problem, such as simultaneous localization and mapping (SLAM) algorithm and intelligent probability-fingerprint (PFP). Atia et al. [
Track navigation positioning method is to start from the initial point. You need to determine the direction of travel and add exercise data to find the position of the next point. This technology relies on the built-in sensors of modern smartphones, such as acceleration meter, gyroscope, magnetic meter, etc., which contain a large amount of instantaneous motion measurement information. With the inertial measurement units (IMU) of these smartphones, it is possible to abstract the movement state of the user [
As shown in
There are three types of non-cooperative positioning algorithms [ Signal-Based Positioning Algorithms:
RSS: The measurement of this method is the signal strength, which means that a higher signal intensity indicates that the distance between the transmitter and the receiver is shorter. A received signal strength indicator (RSSI) is an indicator obtained by signal processing of RSS, which its value is positive. And the distance between the transmitter (TX) and the transmission machine (RX) is determined by the RSSI and signal path loss. The most common formula for calculating the relationship between the power and distance of the calculation signal is to lose the path of the number method [ RSRP and RSRQ: Reference signal-received power (RSPR) is an indicator for measuring the long-term-evolution (LTE) network coverage effect. Theoretically, the RSPR obtained by the terminal measurement is equal to the transmission power of the cell-specific reference signals (CRS). Reference signal-received quality (RSRQ) is a comprehensive indicator of reference signal load and quality. The unit is dB, the calculation formula is shown in
Among them, N represents the reference signal corresponding to the bandwidth of the frequency point. Therefore, the algorithm measures the LTE reference signal power and quality distributed on the signal block transport size (BTS) tower. The signal-noise ratio is the ratio of carrier power to interference power, which is a better standard than power. The authors of [ Direction-Based and Angle-Based Positioning Algorithm:
AOA: The key of positioning algorithm based on the direction and angle is to determine the angle between the transmitter and the receiver. The positioning method of AOA generally uses an antenna array or multiple receivers to obtain the sending angle of the adjacent node signal, and then obtain the orientation line of both parties.
But it requires complex hardware and accurate calibration. And it is impossible to ensure the positioning accuracy of the NLOS, so this method is not suitable for the indoor environment with a complicated situation. The emergence of Bluetooth 5.1 technology can improve the performance of direction-based and angle-based positioning algorithm [ Distance-Based Positioning Algorithms:
TOA and TDOA: The key about this algorithm is to measure the time of signal sending and receiving and thus obtain the distance between them. The distance between the target node and the fixed anchor node is measured by multiplying by time and signal transmission speed (
Among them,
The TOA algorithm [
Time difference of arrival (TDOA) algorithm [
The estimation error provided by this type of positioning algorithm is the least, but the positioning range of this algorithm is relatively small, and it can only cover 3 to 15 m [ FOA and FDOA: Frequency of arrival (FOA) refers to the signal frequency estimated by the sensor from the received radio waves, and then in using these frequency parameters to obtain information about the location of the wireless signal. The frequency difference of arrival (FDOA) makes use of the frequency difference between the transmitted and received signals. Ding so combine FOA and weighted multidimensional standard algorithms to form a closed positioning solution to reduce the difficulty of calculation [
There are many types of collaborative localization algorithms, such as the bayesian algorithm, factor- graph-based localization algorithm, and SPAWN collaborative localization algorithm.
Bayesian Algorithm:
In the Bayesian algorithm [
Among Factor-Graph-Based Algorithm:
The factor-graph algorithm is a commonly used probability-based localization algorithm. The algorithm can effectively use the surrounding reference nodes to assist the end nodes to achieve higher accuracy positioning. Chauchat et al. [
Choosing the right algorithm depends on various factors, such as accuracy, cost, and implementation environment. For different types of positioning algorithms, we compare them from the following aspects, such as Precision, Calculation, Performance, and Application. The comparison results are shown in
Algorithm type | Algorithm | References | Technology | Precision/ Calculate ability | Execution | Application |
---|---|---|---|---|---|---|
Signal-Based positioning algorithms | RSS/RSSI | [ |
BLE/Wi-Fi | Meters/Medium | Prone to multi-path, environmental noise | To track indoor positioning, objects, assets |
PSRP-PSRQ | [ |
Cellular/Where net | Meters/High | Need cellular | Intelligent building, indoor climate, target tracking, security monitoring etc. | |
Direction/Angle | AOA | [ |
BLE5.1 | Decimeters to meters/High | Not for NLOS | Suitable for indoor environments |
Distance-Based positioning algorithms | TOA/TDOA | [ |
UWB | Decimeters/ Medium | Los is required/Larger bandwidth and easier than TOA | Indoor coverage applications with high accuracy |
FOA/FDOA | [ |
UWB | Centimeters/ Medium | Higher delay in UWB | Indoor environment elderly, with higher frequence | |
Cooperative positioning algorithms | Bayesian algorithm | [ |
Cellular/Wi-Fi | Centimeters/High | Calculate multiple information sets | Node selection balance problem |
Factor-Graph-Based algorithm | [ |
Cellular/Wi-Fi | Centimeters/High | Node mobility | The number of reference nodes can be visible |
Indoor positioning technology requires communication technology to transfer data between sender and receiver. These positioning enabling technologies are classified according to different standards and can be divided into short-distance transmission technology and long-distance transmission technology [
According to the different application scenarios and their respective characteristics, the popular technologies for indoor positioning are discussed and compared. These types of wireless technologies are influenced and limited by signal characteristics, such as, Dispersion, reflection, diffraction, and propagation are the basic factors that affect the signal [
Rage | Techno logies | Measurement methods | Precision | Advantages | Disadvantages | Application |
---|---|---|---|---|---|---|
Below 20–50 meters | Wi-Fi | RSS fingerprint/ proximity trilateration angle | 10–100 meters | Low-cost indoor coverage | The database is required for Fingerprinting. Low precision | Track indoor location objects, associative |
Below 100 meters | Bluetooth | RSSI theoretical propagation model. RSS fingerprint | 2–5 meters | Power consumption: low power consumption Equipment is relatively cheap | Low Latency, Unlimited Interference. High density is required. security breach | Suitable for indoor environments such as hospitals and shopping malls |
up to 1000 meters | RFID | Proximity detection, fingerprint, trilateration, scene analysis | 1–5 meters | High portability and provides identifi- cation and positioning throughout almost |
Affected by temperature and humidity | Indoor application |
10–15 meters | UWB | AOA/TOA/TDOA | 0.1–1metres | Transfer more information than lower time precision (12–36 inches) | Developing Technology--Non-Direct Aiming Short-Range Problem | High-precision indoor coverage applications |
2.4 GHZ | Zigbee | RSS/PDOA | 1–10 meters | Indoor coverage, low power consumption, low cost | Low accuracy | Intelligent Building. Indoor climate control transportation and logistics. Target Tracking. Security monitoring, etc. |
Below 100 meters | Visible light communication (VLC) | PDOA/Proximity RSS | Depends on the scope link | Indoor coverage, low cost, and low power consumption are affected by the multipath effect | Robots for indoor environments, higher frequency logistics applications for industry | |
indoor | Ultrasonic positioning technology | RSS/INS | cm level | Centimeter-level accuracy, low cost, simple operation, and high confidentiality | Cost of maintenance, operation, cooling. Errors increase over time | Commonly used for underwater robot positioning |
10 km-urban, 40 km-rural | Sigfox | RSSI for Localization | Meter-level | Low power, long range | Not enough implementation | M2M and IOT applications |
5 km-urban, 20 km-rural | LoRa | TDOA and Best Fit for Localization | Meter-level | Low power, long range | Not enough implementation | Indoor application |
8 km-urban, 35 km-rural | Cellular | OTDOA, ECID and 5G | Meter-level | Long range, high accuracy | Synchronize among station | 2G\3G\4G\5G |
1. Wi-Fi
The main purpose of Wi-Fi is to access the Internet through fixed access points or hotspots. Its equipment follows the IEEE802.11 protocol. Since its development in 1999, its transmission rate has been getting higher and higher, from the earliest 2 Mbps to the current Gbps, and the transmission speed has increased four times in densely populated areas with lower energy consumption. In a Wi-Fi-based system, simple packets of data can be sent to multiple Wi-Fi access points in the facility, which report the read time and strength to the backend, which uses algorithms to calculate the location. The positioning accuracy of Wi-Fi indoor positioning technology is 5–10 meters.
Although Campeón et al. [
2. Bluetooth technology
Bluetooth is a short-range radio wave technology with extremely low power consumption and universal use. It is commonly used in intelligent navigation applications in shopping malls, hospitals, office buildings, and other places. However, for complex space environments, the stability of the Bluetooth positioning system is slightly poor, and it is greatly interfered with by noise signals. Its latest version allows transfer rates up to 24 Mbit/s and is license-free between 2.4 and 2.485 GHz. Bluetooth uses the generic name of the IEEE802.15.1 specification, which defines a global standard for wireless transmission of information (voice and data) between different devices over a secure and license-free short-range radio frequency link [
3. RFID
RFID positioning technology uses radio frequency to exchange data through non-contact two-way communication through electromagnetic signals to achieve the purpose of mobile device identification and positioning. The RFID system is divided into three types: active, passive, and semi-passive. Passive RFID works at distances of up to 10 meters. Active RFID tags can act as beacons to transmit data up to 100 meters [
4. UWB
Ultra-Wideband (UWB) technology is a new type of short-range high-speed wireless communication technology that has attracted much attention. Data bandwidths in the gigahertz range can be achieved by transmitting data by sending UWB pulses at or below nanoseconds. Generally, the distance between the target node and the fixed anchor node is calculated using TDOA and TOA algorithms instead of the RSSI algorithm. UWB is mainly affected by the multi-path effect, excessive delay, clock drift, signal interference, and system estimation. In response to these challenges, Poulose et al. [
5. ZigBee
ZigBee is a new type of wireless communication technology, which is suitable for a series of electronic components and devices with a short transmission range and low data transmission rate, its operating frequency of ZigBee is between 868 MHz and 2.4 GH [
6. VLC
VLC (Visible light communication) uses optical signals for indoor localization has great potential, and visible light communication provides a reliable solution for indoor localization problems [ Ultrasonic Positioning Technology
Acoustic localization technology estimates the location of objects by emitting and capturing sound. It has unidirectional and reflective distance measurement methods. The reflection method uses triangulation to determine the location of objects. It is more durable than signal-based techniques in terms of multi-path effects. It has a simple structure and high precision. Its advantages are centimeter-level accuracy, low cost, simple operation, and high confidentiality, and it is more suitable for indoor positioning [ Sigfox
Sigfox is a low-power wide area network (LPWAN) operator that uses ultra-narrow-band (UNB) technology to transmit low-bit data rates of about 100bps over long distances. Equipped with software-defined cognitive radios and connected to backend servers via IP-based networking. The UNB function helps Sigfox cover a large amount of equipment with fewer base stations. Sigfox proposes positioning in areas where Wi-Fi and GPS are inaccessible and not accurate enough as a complementary technique [ LoRa
LoRa is a LPWANed specification proposed by the LoRa Alliance, which is known for its resistance to multi-path, interference, and doppler effects [ LTE
A cellular network is a mobile communication hardware architecture, which is mainly composed of three parts: mobile station, base station subsystem, and network subsystem. LTE provides a wide range of positioning methods and protocols according to the accuracy requirements of 2G to 5G systems. Some 5G -based protocol algorithms have been implemented, but the infrastructure is incomplete, and the application of this positioning technology is not wide. Li et al. [
Indoor positioning is divided into two ways, cooperative positioning and non-cooperative positioning, according to whether the spatial cooperation between adjacent mobile nodes and the temporal cooperation from the previous state of the mobile nodes themselves are utilized.
Non-cooperative positioning: non-cooperative positioning is to use communication and positioning between the device and the anchor node with a known location, and no additional equipment is involved in the positioning process. The scenario of non-cooperative positioning for home-based care applications is shown in
Cooperative positioning: Compared with traditional positioning technology, cooperative positioning uses the measurement information between unknown nodes to obtain relative position information between nodes, expands the positioning range, improves positioning accuracy, and reduces energy consumption. Cooperative positioning can solve the problem of obstacles in the link between the mobile terminal and the base station.
The dark and light-colored figures in the figure represent the position of the mobile node at the previous time and the current time, respectively, and the cooperative and non-cooperative measurements are represented by dashed and solid lines, respectively.
The principle is the dark-colored old man can determine his position by visiting the three base stations. Due to the communication distance and the building, the light-colored old man cannot directly communicate with the three base stations, but he can accurately access the information of the dark-colored old man.
Implementation process: An important prerequisite is that the target node has Pear-to-Pear wireless communication capabilities, and can obtain connectivity, RSS, TOA, AOA, and other distance angle measurements through wireless signals with neighbors. In the practical application of smart elderly care, this precondition can be easily solved. The communication receiver can be worn on the body of the elderly, which is convenient and compact, easy to carry, and has low power consumption. It has been put into use on many occasions.
There are two types of collaboration, space collaboration, and time collaboration. The relative distance and relative angle are measured, and the additional information in the space obtained by these measurements can enhance the accuracy and robustness of the positioning system. This type of cooperation is called spatial cooperation. The performance can be further improved if the mobile node can also combine its previous state. Using the states of nodes at different times, this type of cooperation can be called temporal cooperation. In the cooperative positioning system, if the location information of one of the elderly people in the system is known, the relative distance and relative angle of the unknown elderly can be obtained through space collaboration. However, if we master the position information of the elderly at the previous time, we can combine our previous state to enhance the accuracy and robustness of the positioning system.
The first application of collaborative positioning to smart elderly care is in nursing homes. Based on the concept of the Internet of Vehicles [
Positioning query can be realized: real-time position query and monitoring of the elderly, grouped position query, etc., to help the staff to manage the real-time positioning of the elderly and prevent the occurrence of missing and other situations.
Track playback: The walking track of the elderly is automatically collected to the cloud, which can be returned to visit. In the event of a discharge accident, the track can be viewed to help the tour.
Passive deployment: The entire solution does not require network deployment. Also does not need to be connected to the power supply.
SOS for help: Wear a smart terminal with an SOS button for the elderly, in case of emergency, press the SOS button to send real-time alarm information to the background.
In addition, cooperative positioning algorithms are divided into centralized [
The current cooperative positioning method has high positioning accuracy and less energy consumption. It solves the positioning problem when the number of anchor nodes is small and sparsely distributed in the network and has a good application prospect.
In this section, the common evaluation indicators of indoor positioning technology home-based care are introduced, and then the challenges and solutions of indoor positioning technology home-based care are discussed
The indoor positioning evaluation index can effectively explain system efficiency and system performance, and comprehensively evaluate the system. Adler et al. [ Accuracy: Accuracy refers to the approximate degree of the estimated position and the actual position, while repetition accuracy refers to the approximate degree of the repeated measurement data over some time. There are many factors affecting accuracy and repetition accuracy, such as noise interference, multi-diameter effect, signal source, and so on. Accuracy and repetition accuracy is regarded as the same concept in the indoor positioning technology of real-life and home-based care. As one of the most basic factors in the evaluation criteria for indoor positioning system, the mean square error (MSE) or root mean square error (RMSE) between the estimated position and the actual position error value is used to measure the quality of the positioning system. The formula of mean square error and root mean square error are Energy efficiency: The realization of high-precision and high-efficiency positioning under the premise of low energy consumption affects whether the positioning system can be accepted by users. The reasons affecting energy consumption include signal transmission and reception interval and transmission power. The influence of signal receiving intervals can be solved by using low-power Bluetooth and UWB positioning technology in passive or active mode. The impact of sending efficiency can be managed by outsourcing tasks or using lightweight algorithms. Coverage: The coverage of the indoor positioning system refers to the spatial expansion that can ensure the system’s performance [ Latency: Latency indicates the clock cycle required to execute an instruction, so the less the delay, the better. The real-time positioning system should be able to determine the position of the object in a limited time (milliseconds) and should not exceed a certain error ratio. Due to the influence of environmental noise, it is difficult to achieve high-precision real-time positioning. Delay is the main factor in time-sensitive services related to people’s lives, such as the real-time alarm of the elderly falling in home-based care [ Interference: The interference of the indoor positioning system refers to the interference factor that affects the positioning. The positioning system is usually composed of one or more devices. These devices are installed in mobile nodes or as deployed infrastructure. Due to the special nature of home care, the fixed infrastructure must be arranged as high as possible to ensure aesthetics and produce less interference with the positioning system. Therefore, these fixed infrastructures must be installed in places that are not easy to be detected. On the other hand, to achieve the safety and comfort of the elderly, the mobile positioning equipment carried by the elderly also needs to be carried out covertly without interference. Universality: Universality refers to the ability for change the number of nodes or changes the size of system coverage. If the positioning system cannot provide position estimation in the extended area of the original design area, the positioning system cannot be extended. Universality is a huge challenge for the positioning system because it needs to monitor lots of nodes at the same time, which may lead to the congestion of information transmission and the loss of accuracy. Commonality/Timeliness: Commonality in the indoor positioning evaluation standard means that users do not need any proprietary hardware to use it. Considering the particularity of many users, this standard is very important in home-based care. For example, compared with UWB and ZigBie positioning technologies, Wi-Fi and Bluetooth positioning technology are two technologies that can be used by almost all users using smartphones. Timeliness means whether real-time access can be achieved when positioning services. Robustness: The robustness of the positioning system refers to the anti-interference ability of the system. This index is very necessary for the consistency of the positioning system. In the application of underground mines, humidity, channel shape and materials will affect the RF signal [ Power consumption: Power consumption is a major factor for market of the home-based care to accept indoor location services, so it needs to be estimated in advance. Costs generally include direct costs and hidden costs. The direct cost includes equipment and infrastructure, such as fixed anchor nodes, servers, special software, etc. Hidden costs include time and human resources that should be considered when implementing and installing products, as well as energy consumption, battery life, and monthly maintenance fees. The power consumption of different devices varies greatly. Compared with fingerprint identification and positioning system, Bluetooth and Wi-Fi Positioning systems consume less energy. The more nodes, the greater the power consumption of the positioning system. A few precise positioning systems can achieve the accuracy of the positioning under the premise of low power consumption. Security and reliability: When providing location-based services in home-based care, security and privacy are very important issues. Location information (especially real-time positioning information), daily routine information etc., are considered extremely important privacy information. Because it is collected and stored by the positioning system, it is easy to leak. To solve the problem of capturing, storing, and using many positioning data about the elderly at home, it is necessary to ensure that the collected data are used correctly [
where
We currently face many challenges in the indoor positioning for home-based care, and there are a variety of factors that contribute to these problems. There are challenges for which solutions are currently available, and challenges for which there are no proven solutions themselves. The specific challenges faced are shown in Lack Universality: Due to the impact of different service users, housing structures, and other factors, the home-based care positioning system needs to rely on different positioning technologies, resulting in coverage, maintenance cost and positioning accuracy problems. Some companies try to combine Bluetooth, UWB or Wi-Fi in their positioning products to achieve better interoperability. However, the indoor positioning system lacks an open standard that can cover all applications and meet the needs of users. Therefore, the interaction between different components is one of the main challenges in the field of indoor positioning. Developing an interoperable middleware for the interaction between heterogeneous components and standardized devices and protocols can solve this problem [ Measuring noise and Position Detection Noise: It is difficult to model the statistical distribution of observations and noise because of the noise interference when measuring information. The movement of the sick elderly is irregular and cannot be detected by the odometer like a car. There are two ways to solve this problem: one is to directly eliminate environmental noise and surrounding obstacles; the other is to process signals to reduce noise errors. Since the indoor positioning of home-based care can not eliminate the external environmental obstacles and noise in the real scene, we can use the two methods to add some algorithms to reduce the signal interference. For example, median filter [ Privacy and Reliability Issues: Currently, there is no mature solution to the security and privacy issues involved in the indoor positioning system. For special users of the elderly, it is easy to disclose the user’s location due to unsafe data exchange in the process of cooperation, allowing illegal criminals to take advantage of it. In addition, the increase in the number of virtual operators and service providers will also add unsafe factors to indoor positioning applications [ Positioning Accuracy Theory Limit: When positioning the elderly for home-based care, it is inevitable that they will be affected by various factors, such as NLOS conditions, multipath propagation, signal bandwidth and network geometry. In order to improve positioning accuracy, experts have also conducted relevant research. Ellahi et al. [
Challenge types | Factor | Solution | Author and years |
---|---|---|---|
Lack universality | Different service users, housing structures | Developing an interoperable middleware for the interaction between heterogeneous components and standardized devices and protocols | Gu et al. [ |
Measuring noise and position detection noise | Noise interference | one is to directly eliminate environmental noise and surrounding obstacles; the other is to process signals to reduce noise errors. | Pelka et al. [ |
Du et al. [ |
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Ko et al. [ |
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Privacy and reliability issues | Unsafe data exchange | No mature solution | |
Positioning accuracy theory limit | NLOS\multi-path propagation\signal bandwidth\network geometry | Improved algorithms | Ellahi et al. [ |
Given the above challenges in the indoor positioning system for home-based elderly care, some methods are adopted to meet the challenges and improve the performance of the indoor positioning system (IPS) for home-based care. The following is an introduction to three aspects: Multi-agent, Strategy Mechanism and Network Optimization.
Multi-Agent: Multi-agent [ Strategy Mechanism: At present, the positioning algorithm and technology are relatively mature, and the room for improvement is not very large. We can improve the performance of the positioning system by incorporating some strategic mechanisms, such as game-theoretic [ Network Optimization: Network performance is also one of the main factors affecting indoor positioning systems. At present, the world has set off a wave of digital transformation of the industry, digital is the foundation, network is the support, intelligent is the goal. On this basis, improve network optimization and maintain a good transmission rate. Strengthen the epidemic prevention system and relieve privacy issues. Optimization techniques: Considering the problems such as error iteration caused by using some algorithms in the localization process, we can add some optimization algorithms, such as the Chimp optimization algorithm (ChOA) [
Indoor positioning solutions are designed to overcome the limitations of GPS. Although indoor solutions have made slow progress in the past decade, their accuracy has increased from the meter level to the centimetre level. Considering unlimited applications and high demand for localized services, many companies have entered this market and adjusted their products using LBS service-based positioning [
Since 2000, driven by the government, the development of mobile communication technology has shown an upward trend, and so have mobile location services. And the trend of population aging and the development of mobile technology ensure basic consistency.
Develop | Method | Precision | Influencing factors | Timeliness |
---|---|---|---|---|
2G | CID + TA | About 550 m | Cell size | Very poor |
E-OTD | 50–300 m | Multi-path | Medium | |
3G | CID + RTT | About 200 m | Cell size | Poor |
OTDOA | 50–200 m | Multi-path | Medium | |
A-CPS | 10–50 m | Poor indoor signal | High | |
4G | ECID | ≤150 m | Cell size and Multi-path | Poor |
OTDO UTDOA | 50–200 m | Multi-path | Medium | |
A-GNSS | ≤10 m | Poor indoor signal | High | |
5G | TOA/TDOA | ≤50 m | Multi-path | Medium |
FDOA | ≤10 m | Signal strength and multi-path | High | |
ECID | ≤15 m | Coverage and multi-path Effects | High |
The following focuses on the candidate technologies for indoor positioning in the 6G mobile communication technology. Including terahertz-assisted indoor positioning, large-scale multi-antenna-based indoor positioning, and indoor positioning technology based on reconfigurable smart surfaces.
Multiple input multiple output (MIMO) [
The 6G system can reconstruct the intelligent surface (RIS) [
Terahertz has abundant spectral resources [
The review provides a safe and feasible home-based care solution and evaluation for the elderly. Firstly, we deeply analyzed the main characteristics of the current home care positioning system and discussed the necessity of precise indoor positioning for the elderly. Secondly, the advantages of device-free positioning are explained in detail. Because of the particularity of the elderly with neurological diseases, for example, the movement burden of the elderly is aggravated by wearing the positioning device, and some elderly cannot wear the positioning device in time because of memory loss, the precise positioning fails to achieve the expected effect. Therefore, we choose the device-free positioning technology and systematically introduce it. Then, the positioning techniques, technologies and methods used in the home-based care positioning system are analyzed and compared. Moreover, the review presents the evaluation metrics for indoor positioning and the challenges faced by researchers working on the indoor positioning of elderly with neurological disorders. Finally, candidate technologies for indoor positioning applications under 6G mobile communication technology are analyzed. And discussed the development opportunities and challenges in the 6G mobile communication network.
In the future, with the development of mobile communication technology, there are still many challenges and opportunities in this field, which require more extensive and professional research by relevant researchers. Neural networks in machine learning can be used to improve the accuracy of positioning systems, and optimization algorithms such as intelligent groups can be combined with positioning technology to improve system efficiency. This recent optimizer provides strong technical support for the performance improvement of the indoor positioning systems, such as the Chimp optimization algorithm, and expert system with the application. Furthermore, the combination of device-free indoor positioning technology and other technologies is used to predict falls and sudden illnesses in the elderly, thereby reducing physical injuries to the elderly with neurological diseases and providing comprehensive protection for the elderly.
The authors would like to extend their gratitude to the anonymous reviewers and the editors for their valuable and constructive comments, which have greatly improved the quality of this paper.