According to dictionary definition, intelligence means the ability to comprehend, reason and learn [14]. Intelligent systems are characterized by the fact that they can produce control actions in a flexible, adaptive, and robust manner, without much prior knowledge of the environment.
The methodology used are computational intelligence method using mathematical algorithms which find hidden patterns in the input data to classify, predict and make decisions.
In intelligent unobtrusive monitoring system for ELIAH, AI techniques can be used for detection of critical events like falls or fire, monitor activity or movement pattern like sitting and lying down, diagnostic and rehabilitation purposes i.a. Remote wearable and ambient sensor systems for movement and activity monitoring will record a massive amount of raw input data which has to be managed and processed to derive relevant information from it [4].
Some of the techniques applied on these data sets after being recorded are signal processing, normalization, feature extraction and AI techniques to obtain a usable AI model. Features are characteristics that describes the raw inputs. A general flow for creating AI models in use for determining outputs are shown in Figure 2-2.
Figure 2-2. Typical flow of activities when creating AI models in use for determining outputs. White boxes indicate parameters and grey boxes indicate algorithms or models for processing and
determining parameters
2.2.1 Threshold-based Classification
Threshold as an AI technique can be applied for various purposes in determining a sought output. For example, in a system that constantly measures pulse, a threshold-based classifier could generate alarms if the pulse is under a certain threshold, or an alarm can be stored in electronical medical records (EMR) if it the pulse has been over or under a threshold limit for a given period, which can indicate some health-related issues. A threshold classifier have been developed by Boyle et al. [15]. The algorithm was used to classify type of activity performed, like lying, standing/sitting or walking/Activities of Daily Living (ALDs), based on torso angle measured by an accelerometer located around the torso as shown in Figure 2-3.
Figure 2-3. The formula used in [15] to calculate torso angle 𝜃 based on acceleration measurement 𝑎 from an accelerometer placed on the torso to classify the activity type performed
2 Literature Study on Existing Techniques in the Field of Unobtrusive Monitoring of Elderly as well as Related Fields
2.2.2 Artificial Neural Network (ANN)
Artificial neural networks (ANNs) consists of nodes of mathematical functions, modelled in the way the human brain and neurons works. Input data, or features is fed into the network in form of inputs, and the ANN determines outputs based on the relationship between these. ANN are commonly used for activity classification [16].
Using an ANN, Zhang et al. [17] developed a portable device including a measurement system shown in the schematic overview in Figure 2-4. The system identifies type of intensity of a locomotion by placing a composition of multiple pressure sensors between insoles of shoes.
The portable device manages to record and measure foot-ground contact information in every step. Studying various activities, the ANN classifier embedded in the device correctly identified the type of activity with an accuracy > 97 %, as well as predict the speed of walking and running.
Figure 2-4. Overview of the system classifying the intensity of human locomotion as well as speed of walking and running using pressure sensor in the insoles of shoes in the study by Zhang et al. [17]
The ANN structure established for the study by Zhang et al. [17] involves two feedforward back-propagation ANN consisting of two hidden layers and one output layer shown in Figure 2-5. The number of nodes in the two hidden layers of the study by Zhang et al. shown in Figure 2-5 were decided optimally by the minimization of mean square output of a training set. Figure 2-5 also illustrates that both weights calculated and outputs from the preceding node is the input to the next node handled by an activating function.
2.2.3 Support Vector Machine (SVM)
Given a set of input data, support vector machines (SVM) can classify them into several categories based on an optimal separating decision hyperplane, found by training the classifier algorithm. SVM is moderately used in activity classification studies [16]. SVM can project the data points in the original space featured in, to another higher dimension by applying kernel methods which converts the inputs in the input space to a feature space, and from there find hidden patterns of the input data points [16].
García-Massó et al. [9] conducted a study to identify physical activity type of spinal cord injury patients equipped with four body-worn 3-axial accelerometer. SVM was one of the classifier algorithms evaluated in the study, among others. SVM produced a classification accuracy of 94 % when using two wrist-, one chest- and one waist accelerometer as shown in Figure 2-6.
Figure 2-6. Placements of the 3-axial accelerometers on the spinal cord injured patients in the study by García-Massó et al. [9]. A SVM classifier produced the best accuracy for identifying physical
activity type when applying all data sets from all the four accelerometers combined
2.2.4 k-Nearest Neighbour (kNN)
The kNN (k-Nearest Neighbour) algorithm are applied in several classification studies related to activity detection [4]. The algorithm uses a multi-dimensional feature space, where each dimension represents a different feature [18]. All the training data points that has been collected from observation or experiments are plotted in the feature space. The aim is to identify the 𝑘-nearest points (or neighbours) of the training data, which contain unknown window distances in-between the observations. The observations are classified by the majority of the kNN’s. The value of 𝑘 typically varies from 1 to a small percentage of the training data and is determined preferably using cross-validation procedures or trial and error [16]. Figure 2-7 shows data points plotted in a feature space, which is inputs of the kNN classifier.
2 Literature Study on Existing Techniques in the Field of Unobtrusive Monitoring of Elderly as well as Related Fields
Figure 2-7. Showing a 2D feature space where different types of activities have been plotted along the feature axes Mean and Standard Deviation [18].
Finding out that the uppertrunck of the body, below the neck and above the waist, is the most suitable region on the body to place sensors for fall detection, Jian et.al developed a wearable system for detecting falls and ALDs using the kNN algorithm [19]. The activities were monitored by a 6 degrees of freedom (DOF) accelerometer and gyroscope combined sensor board located at the neck of a wearable west the subject carried.
Figure 2-8. The architecture of the system developed by Jian et.al. [19]. The sensor signals are processed by the software in a smartphone and alerts relevant actors like family or health care sector,
if necessary
With this approach, the kNN algorithm demonstrated an accuracy of 92,5-100 % when classifying the falls and ADLs, were the fall activities gave the lowest accuracies and walking-turning-walking activity gave the highest accuracy of 100 %.