For elders with dementia, wandering is among the most problematic, frequent and dangerous behavior. Managing wandering behavior has become increasingly imperative due to its high prevalence, negative outcomes and burden on caregivers. We study to propose an active infrared-based method to identify wandering locomotion by monitoring rhythmical repetition of an elder’s indoor motion events. Specifically, we utilize our customized active infrared sensors to collect human indoor motions that will be converted into motion events by using hardware redundancy technique. Each motion event is a directed motion obtained via introducing temporal and dimensions into the spatial motion data. Based on the most cited spatial-temporal patterns of wandering locomotion, a spatiotemporal model is then proposed to identify wandering locomotion from an ongoing sequence of motion events. Experimental evaluation on eight individuals’ real-world motion datasets has shown that our proposed method is able to effectively identify wandering locomotion from repetitive events collected from active infrared sensors with a value over 98% for both accuracy and precision based on properly chosen parameters. Wandering in elders with dementia that follow specific spatiotemporal patterns can be reliably identified by analyzing repetitive motion events collected from active infrared sensors based on the well-known spatiotemporal patterns of wandering locomotion.
Dementia has been identified as a progressive, disabling and chronic disease, affecting 5% of people aged above 65 and around 40% of people aged over 90 [
Traditional solutions to prevent elders with dementia from wandering mainly include imposing physical restraints and/or using medicine. However, the physical or psychological problems caused by physical restraint and the side-effects of neuroleptic drugs make the traditional methods infeasible or ineffective to protect wanderers especially for those individuals who are prone to falling or unsafe wandering [
Preventive measures able to maximize autonomy while minimizing risk are recommended to monitor wandering-prone elders in home, community and care facility environments [
A large number of sensors-based solutions for managing wandering behavior have been proposed by using different techniques [
• First, active infrared (AIR) sensors are used to sense human indoor motions when an individual walks into/out rooms or move across certain crucial area. Compared to those existing systems developed by using switch sensors, UWB sensors, step-count sensors, passive-RFID, pressure sensors [
• Second, all the spatial and temporal dimensions of human motions are integrated to build a spatiotemporal wandering model based on the spatiotemporal patterns of wandering [
• Last, more than one thousand sequences of motions were used to evaluate the proposed method. Experimental results have shown that the proposed method obtains good performance for detecting wandering locomotion with a value over 98% for both accuracy and precision, respectively.
The rest of this paper is organized as follows. In Section 2, we present our proposed method for detecting indoor wandering behavior using AIR based system. In Section 3, we provide the experimental evaluation results conducted on real-world datasets from eight individuals. And in Section 4, we conclude this work.
To keep the completeness, in this Section, we first provide the most cited definition and spatiotemporal patterns for wandering behavior.
Although different attempts have been done for defining wandering behavior, there is no a widely accepted definition so far because wandering behavior is very complex and occurs for a number of reasons. We herein present the latest one proposed by Algase et al. [
We can see from the above definition, wandering locomotion is characterized by its anomalies on walking frequency, repetition and spatiotemporal disorientation. Especially, the spatial aspect of wandering behavior can be divided into several different patterns (lapping, pacing and random patterns) [
• Direct pattern: walking directly from one place to another without any detour;
• Pacing pattern: back and forth motion between any two points, i.e., physical locations;
• Lapping pattern: circuitous motion revisiting some points sequentially along a path;
• Random pattern: haphazard motion without repeating points in traveling sequence.
Among the above patterns, the direct one is regarded as normal and the remaining patterns link to wandering behavior.
Algase [
Detecting wandering locomotion by using active infrared sensors is consisted into two main steps. The first step is responsible for acquiring human indoor motions with active infrared sensors. The second step is in charge of detecting
wandering locomotion through analyzing rhythmical repetition of events from the monitored motions in the first step.
The proposed method uses our customized AIR sensors to monitor human indoor motions. As depicted in
In the customized AIR-based sensor system, the infrared transmitter sends a beam of infrared light (essentially invisible due to its longer wavelengths than those of visible light) which will be received by the infrared receiver. Generally, a continuous signal can be received by the receiver if there is no obstruction (both the static and mobile) between the transmitter and receiver. On the contrary, when a mobile object moves across the scan area of a transmitter, the beam of infrared light will be interrupted (as depicted in
m o t i o n = ( s , t ) (1)
where s denotes the sensor that an elder traverses at time t. An elder’s continuous motions will produce a sequence of motions, i.e., {motion1, motion2, …, motioni, …}, with motioni denoting the i-th motion. Otherwise, further processes are needed as follows.
• If d ≤ dL, a motion is detected which is often caused by infrared-sensitive burst interferences (e.g., transient bright light). The motion, in this case, often has obviously shorter duration than the one of human motion.
• If d ≥ dU, a motion is detected which is often caused by relatively-persistent interferences (e.g., dense smoke). The motion in this case often has obviously larger duration than the one of human motion.
In this paper, the values for dL and dU are experimentally determined based on the average human walking speed of 5.0 kilometers per hour by fully taking elders’ health conditions into account. With a detected motion by leveraging AIR sensor, the following task is to determine the motion-related event. In this work, a motion-related event is semantically defined as a directed motion, which corresponds to entering/exiting a room or a crucial area by an elder. Specifically, we consider only two directions for the directed motions: entering and exiting. Thus, an event (i.e., directed motion) can be represented as follows.
e v e n t = ( m o t i o n , d r t ) (2)
where d r t = ( i n , o u t ) refers to the set of the directions, and in and out denotes the entering and exiting, respectively. We propose a hardware redundancy technique to acquire an event from one detected motion. The hardware redundancy technique is utilized to integrate two pairs of transmitters and receivers in an AIR sensor. For an AIR sensor consisting of T1-R1 and T2-R2, we need to ensure that the scan zones of T1 and T2 are not overlap with each other (see
With the spatial wandering patterns as mentioned previously, a wandering locomotion can be regarded as a sequence of events that follow one or more spatial patterns. To build a spatio-temporal model for detecting wandering behavior, we need to examine the temporal factors of such a behavior.
Given an event sequence of {…, motioni−2, motioni−1} that follows one of the pacing, lapping and random spatial patterns. For a new event motioni if the time difference gi = ti − ti-1 of these two events motioni−1 and motioni is larger than a given threshold gT, a finished event sequence {…, motioni−2, motioni−1} has been detected. Otherwise, we obtain an ongoing event sequence {…, motioni−2, motioni−1, motioni}. It is worth noting that the threshold gT is experimentally determined. For any one shortest event sequence, such as {motioni−1, motioni}, we can calculate a score for it as scorei = 1/gi. Obviously, the lower the time difference ti − ti-1 is, the larger the score scorei achieves. The reason is that wandering locomotion is always frequent, thus the small value of ti − ti-1. Therefore, for a sequence consisting of n events, the total score can be calculated according to Equation (3).
s c o r e = ∑ i = 1 n s c o r e i (3)
Now, we obtain a spatiotemporal wandering model as follows.
Wand = ( S , T ) | S = { Lapping, Pacing, Random } T = { g T , score } (4)
where S is the spatial set consisting of all spatial patterns that a wandering locomotion may follow, and T is the temporal set containing threshold and score for an event sequence. Algorithm 1 provides the algorithm for detecting wandering locomotion based on repetitive events collected from the AIR sensors in indoor environment.
In Algorithm 1, the Spatial_Wand procedure is used to identify the spatial pattern of an event sequence, which will be presented in Algorithm 2. For a given event sequence consisting of n events, if Algorithm 1 outputs n − 1 “L” (“P”), a wandering locomotion that follows the lapping (pacing) pattern has been detected. Otherwise, a wandering locomotion that follows random pattern has been detected.
In the evaluation, we recruited eight volunteers to collect motion data by using our customized AIR sensors. Overall, there are totally eight datasets collected from eight volunteers. In each dataset, there are the number of the total sequences, the number of the sequences following the lapping, pacing and random patterns, and the wandering ratio of the wandering sequences in the total sequences (as shown in
The direct sequences in
Algorithm 1. The event-based wandering detecting algorithm.
Algorithm 2. The Spatial_Wand procedure.
Motion sequences | Datasets | |||||||
---|---|---|---|---|---|---|---|---|
D-1 | D-2 | D-3 | D-4 | D-5 | D-6 | D-7 | D-8 | |
The total number | 143 | 152 | 146 | 140 | 153 | 146 | 144 | 145 |
Direct | 64 | 48 | 74 | 94 | 57 | 49 | 58 | 47 |
Random | 42 | 44 | 48 | 34 | 63 | 69 | 57 | 57 |
Pacing | 25 | 36 | 15 | 8 | 15 | 16 | 20 | 26 |
Lapping | 12 | 24 | 9 | 4 | 18 | 12 | 9 | 15 |
Wandering Ratio | 55.24% | 68.42% | 49.32% | 32.86% | 62.75% | 66.44% | 59.72% | 67.59% |
Furthermore, in order to quantitatively evaluate the proposed method, we asked three geriatric domain experts to manually label all the sequences in each dataset. If the majority of the experts (i.e., at least two of them) think that a motion sequence is an outlying motion event (i.e., probably a wandering locomotion), it is labelled as an outlying one. The volunteers comprise of one male geriatrician (ages 55) and two physicians (ages 43 and 27 respectively). Three experts independently labelled each sequence after being trained to identify three different patterns of wandering locomotion. These manually labelled outlying events serve as the ground truth in the experiments.
The evaluation metric we use is accuracy and precision. In practice, a detected sequence falls into one of the four categories:
• True Positive (TP), which correctly identifies an outlying sequence as an outlier;
• False Positive (FP), which incorrectly identifies a normal sequence as an outlier;
• False Negative (FN), which incorrectly identifies an outlying sequence as normal; and
• True Negative (TN), which correctly identifies a normal sequence as normal.
Accordingly, we define the accuracy acc and precision pre as follows:
a c c = T P + T N T P + F P + T N + F N , and p r e = T P T P + F P .
The experiments are run in Matlab on an Intel Core i7-6500U PC with 8 GB RAM running Windows 10.
We quantitatively evaluate the proposed method by calculating its accuracy and precision values. The values for parameters dL and dU are set as 0.5 second and 1.5 seconds respectively. In addition, the value of threshold gT is set as 2 minutes. It means that for an already arrived event sequence {…, motioni−2, motioni−1} and a new motioni, if the duration of motioni−1 and motioni is larger than 2 minutes, we have a finished event sequence {motion1…, motioni−2, motioni−1} and a new sequence has already started at motioni.
In order to differentiate all the wandering events with different spatiotemporal patterns,
Experimental evaluation conducted on eight individuals’ real-world motion datasets has demonstrated that our proposed method achieved a value of over 98% for both accuracy and precision on detecting wandering locomotion based on the properly chosen parameters. However, we need to pay attentions to the following two situations when apply our proposed method for detecting dementia-related wandering of elders.
• First, an event sequence following lapping pattern is uniquely associated with a certain sensor, and an event sequence following pacing pattern may contain one or more pacing events. As a result, the events following lapping patterns can always be detected by using our AIR sensors.
• Second, in practice, there is often relatively small difference between the direct and random patterns because an event sequence corresponding to doing housework may move across several areas or doors, leading to misdetection between the direct and random motions.
Direct | Random | Pacing | Lapping | Accuracy | Precision | |||||
---|---|---|---|---|---|---|---|---|---|---|
Gt | Dt | Gt | Dt | Gt | Dt | Gt | Dt | |||
D-1 | 64 | 63 | 42 | 44 | 25 | 24 | 12 | 12 | 0.9931 | 0.9877 |
D-2 | 48 | 46 | 44 | 45 | 36 | 37 | 24 | 24 | 0.9870 | 0.9815 |
D-3 | 74 | 72 | 48 | 47 | 15 | 19 | 9 | 8 | 0.9865 | 0.9737 |
D-4 | 94 | 94 | 34 | 35 | 8 | 7 | 4 | 4 | 1.0000 | 1.0000 |
D-5 | 57 | 55 | 63 | 67 | 15 | 14 | 18 | 17 | 0.9871 | 0.9800 |
D-6 | 49 | 47 | 69 | 70 | 16 | 17 | 12 | 12 | 0.9865 | 0.9802 |
D-7 | 58 | 57 | 57 | 60 | 20 | 18 | 9 | 9 | 0.9931 | 0.9886 |
D-8 | 47 | 46 | 57 | 57 | 26 | 27 | 15 | 15 | 0.9932 | 0.9900 |
In this paper, we have proposed an AIR based method for detecting elders’ wandering behavior, where a group of customized AIR sensors were used to monitor elders’ indoor motions. With the monitored motions, a hardware redundancy technique has been proposed to convert each motion into a motion event. Then a spatiotemporal wandering model has been proposed to identify wandering locomotion from an ongoing sequence of motion events based on the spatial-temporal patterns of wandering behavior.
Using more than one thousand motion sequences collected from eight volunteers, our proposed method has been evaluated to examine its effectiveness on detecting wandering-related anomalous movement by analyzing repetitive motion events. The experimental results show that our proposed method is applicable to the event-based detecting of wandering locomotion by using AIR sensors. A value of over 98% for both accuracy and precision has been achieved based on properly chosen parameters.
In summary, wandering in elders with dementia that follows specific spatiotemporal patterns can be reliably identified by analyzing repetitive motion events that are collected from AIR sensors based on the well-known spatiotemporal patterns of wandering locomotion.
The authors would like to thank all the anonymous reviewers and the editor-in-chief. This work is partially supported by the National Natural Science Foundation of China (No. 61562075), the Natural Science Foundation of Gansu Province (No. 1506RJZA269), the Fundamental Research Funds for the Gansu Universities (No. 2015B-002), the Fundamental Research Funds for the Central Universities (Nos. 31920150081, 31920180114) and the Gansu Provincial First-Class Discipline Program of Northwest Minzu University.
Lin, Q., Zhao, W.C. and Wang, W.L. (2018) Detecting Dementia-Related Wandering Locomotion of Elders by Leveraging Active Infrared Sensors. Journal of Computer and Communications, 6, 94-105. https://doi.org/10.4236/jcc.2018.65008