Figure 3 - uploaded by Dan Istrate
Content may be subject to copyright.
provides a synoptic block-diagram scheme of the software architecture of the ADL recognition platform, it is implemented under Labwindows CVI and C++ software. It is developed in a form of design component. We can distinguish three main components, the acquisition module, the synchronization module and the fuzzy inference component. It can run off-line by reading data from a data base or online by processing in real time data acquired via the acquisition module. To avoid the loss of data, a real time module with two multithreading tasks is integrated in the synchronization component. The platform is now synchronized on Gardien
Source publication
Learning and recognizing human activities of daily living(ADL), is very useful and essential to build a pervasive home monitoring system. These monitoring technologies are indispensable for developing the next generation of smart houses. In this paper we describe a fuzzy logic system for recognizing activities in home environment using a set of sen...
Citations
... Design of the discriminator Itu mungkin beberapa dasar dari fuzzy logic yang berupa framwork untuk membantu me-monitoring melalui network[7] ...
... Website real time untuk memantau perkembangan kondisi pasien, [5] menggunakan logika fuzzy untuk mengenali dan memantau kegiatan aktifitas dari manula dengan menggunakan teknologi sensor. ...
... Several studies have reported important variables for the elderly's QoL prediction by leveraging feature selection analysis and optimization techniques. The strongest predictive variables in the reviewed studies were categorized into three classes: (1) sociodemographic variables [71,[75][76][77][78][79][80][81][82][83][84][85][86] such as age, sex, occupation, weight, ethnicity, monthly revenue, and marital status; (2) supportive and lifestyle factors [16,75,77,79,81,[87][88][89][90][91] such as education level, preventive measures, spirituality status, nutrition factors, physical activity/exercise, residence status, household factors, psychosocial support, domestic violence, living alone, having a partner, family support, number of family member, level of family member caring, accessibility of safe water, social service accessibility, social relationships, insurance situation, justice, and volunteer service; and (3) physical and mental variables [92][93][94]. such as underlying diseases, number of chronic diseases, hopelessness and depression, discomfort and general malaise, perceived health status, functional status, physical symptoms, and the number of symptoms. ...
... Various machine learning algorithms have been proposed for context-aware HAR. Generally, there are five types of algorithms, namely the fuzzy logic-based [12,13], probabilistic-based [14,15], rule-based [16,17], distance-based [18,19], and optimization-based approaches [20,21]. ...
... To begin the discussion with fuzzy logic-based algorithms, a fuzzy rule-based inference system using fuzzy logic (FL) was proposed for the HAR of six activities: exercising, laying, sitting down, standing up, walking, and sleeping [12]. An experimental analysis of the system with a one day case study showed an accuracy of 97%. ...
... There are two major types of feature extraction. Major works [12][13][14][15][16][17][19][20][21] utilized the traditional feature extraction process. There has been less discussion (e.g., [18]) of automatic feature extraction using a deep learning algorithm, which may extract more representative features and eliminate the domain knowledge of all human activities; • ...
Human activity recognition (HAR) is crucial to infer the activities of human beings, and to provide support in various aspects such as monitoring, alerting, and security. Distinct activities may possess similar movements that need to be further distinguished using contextual information. In this paper, we extract features for context-aware HAR using a convolutional neural network (CNN). Instead of a traditional CNN, a combined 3D-CNN, 2D-CNN, and 1D-CNN was designed to enhance the effectiveness of the feature extraction. Regarding the classification model, a weighted twin support vector machine (WTSVM) was used, which had advantages in reducing the computational cost in a high-dimensional environment compared to a traditional support vector machine. A performance evaluation showed that the proposed algorithm achieves an average training accuracy of 98.3% using 5-fold cross-validation. Ablation studies analyzed the contributions of the individual components of the 3D-CNN, the 2D-CNN, the 1D-CNN, the weighted samples of the SVM, and the twin strategy of solving two hyperplanes. The corresponding improvements in the average training accuracy of these five components were 6.27%, 4.13%, 2.40%, 2.29%, and 3.26%, respectively.
... Membership function values range between 0 and 1 (Diker, 2019). As mentioned before, membership function typologies (Medjahed et al., 2009) can be defined as follows: ...
... The first part is the rule block or fuzzy control rules, which is a set of rules that define the relation between input and output variables of each fuzzy set using the "IF-THEN" conditional proposition. The "IF-part" of the rule is introduced as the antecedent or premise (Medjahed et al., 2009) while "THEN-part" is introduced as a consequent or conclusion (Çekmiş et al., 2014). Due to the multiple input variables, there are operators to connect them. ...
... They must be combined in some manner to make a decision (Arabacioglu, 2010). This step is managed by the algorithm (Çekmiş et al., 2014;Medjahed et al., 2009). Despite these steps, Çekmiş et al. (2014) applied an aggregation operation for this process. ...
Purpose
Spaciousness is defined as “the feeling of openness or room to wander” that has been affected by various physical factors. The purpose of this paper is to assess the spaciousness of space to determine how spacious the space is. Furthermore, the study intends to propose a fuzzy-based model to assess the degree of spaciousness in terms of physical parameters such as area, proportion, the ratio of window area to floor area and color value.
Design/methodology/approach
Fuzzy logic is the most appropriate mathematical model to assess uncertainty using nonhomogeneous variables. In contrast to conventional methods, fuzzy logic depends on partial truth theory. MATLAB Fuzzy Logic Toolbox was used as a computational model including a fuzzy inference system (FIS) using linguistic variables called membership functions to define parameters. As a result, fuzzy logic was used in this study to assess the spaciousness degree of design studios in universities in the Iraqi Kurdistan region.
Findings
The findings of the presented fuzzy model show the degree to which the input variables affect a space perceived as larger and more spacious. The relationship between parameters has been represented in three-dimensional surface diagrams. The positive relationship of spaciousness with the area, window-to-floor area ratio and color value has been determined. In contrast, the negative relationship between spaciousness and space proportion is described. Moreover, the three-dimensional surface diagram illustrates how the changes in the input values affect the spaciousness degree. Besides, the improvement in the spaciousness degree of the design studio increases the quality learning environment.
Originality/value
This study attempted to assess the degree of spaciousness in design studios. There has been no attempt carried out to combine educational space learning environments and computational methods. This study focused on the assessment of spaciousness using the MATLAB Fuzzy Logic toolbox that has not been integrated so far.
... For this reason, a common approach is to combine binary sensors with another kind of sensor like cameras, microphones or floor sensors [81,82,168,169]. This idea of designing a monitoring system out of several complementary sensors is called hybrid systems and allows to compensate lacks of one sensor or just to confirm detection in some situations. ...
Tarkett is a global flooring company that developed a piezo-electric sensor encapsulated in the flooring and an embedded system meant to be equipped in nursing home patient rooms. The objective through this industrial project is to build reliable machine learning models able to work in real-time in the embedded system, based on piezo-electric signals, to provide useful information for medical staff to monitor their patients health. Considering different measurement technologies we describe how they affect the original physical signal, as well as different data gathering environments in which several dataset have been recorded. To be able to monitor elderly health state some important recurrent events like walk and some anomalies like falls need to be recognized from floor sensor signals.To this end, the way to process signals into adequate data representation, according to these detection purpose, is also a major challenge. We use a wide feature set based on time series from various signal representations such as Fourier transform, autocorrelation and spectrograms. Using predictive models based on random forests on different experimental datasets we show Tarkett system ability to achieve various monitoring tasks, as well as the relevance of each signal representation and associated features regarding these detection tasks. Nevertheless for these experimental studies to be deployed industrially in FIM Care real installations, machine learning models need to fulfill two crucial requirements. Firstly they have to be confronted with real environment data, meaning to be able to adapt to real installations variability and to activity signal differences between people. In this context we deal with the problem of adapting a predictive model initially trained on experimental data to real data with different empirical distribution. This particular situation in machine learning is known as transfer learning or domain adaptation. We address it by confronting simulated events data to real data on the fall detection task that presents the particularity of extreme class imbalance in real conditions. We investigate the drawbacks of this class imbalance on existing transfer learning methods on decision trees and propose some adaptations to handle this problem. Our contribution is a robust model-based transfer learning algorithm on random forests able to deal with class imbalance and that can also be used to interpret relations between two different domains. Secondly, most of the prediction tasks for elderly monitoring have to work in real time being embedded in an electronic device with limited computational capabilities. Taking into account this kind of constraints while designing a predictive model belongs to a branch of machine learning, known as cost sensitive or budget learning, that became an increasingly active research topic in the past years.We translate embedded system computational resource constraints into a budgeted prediction time framework compatible with decision tree based models and propose an efficient and scalable genetic algorithm considering both feature acquisition cost and evaluation cost allowing to pass from an experimental random forest model to a new simplified one that fits in embedded system resource limits. This algorithm takes advantage of the notion of equivalence between classifiers, meaning models sharing the same decision function but with different structures, to favor feature acquisition cost reduction by exploiting structural variety on decision trees.
... Fleury et al. [17] used the SVM (Support Vector Machine) algorithm to classify data; first, several attributes are selected using the PCA algorithm, and after that SVM is applied to these data. Medjahed et al. [18] provided another method with the fuzzy logic, in which each activity is assigned to one of the various periods and then is defined as a series of rules. The type of activity is also predicted using these rules. ...
In recent years, Smart Cities and Smart Homes have been studied as an important field of research. The design and construction of smart homes have flourished so that this technology is used for more comfort of people and helping their health. This technology is very useful for single and elderly people especially for Alzheimer’s disease patients who are alone at home and need permanent care. A lot of research has been done to detect the activity of users in smart homes. They gather raw data of sensors and use the usual classification algorithms for activity detection. The essence of the time dependency of sensors’ data has been ignored in most research, while considering this is very important, especially in the case of Alzheimer's patients. In this study, a Nonlinear AutoRegressive Network (NARX) is employed to detect the patient’s activity in a smart home. NARX is a recurrent dynamic network, which is commonly used in time-series modeling. The results show that the proposed model detects user activity, with an accuracy of 0.98. Since the high-risk behavior of the Alzheimer’s patient is very unknown; a fuzzy inference system is implemented based on the experience of the Alzheimer's sub-specialist and nurses. The main parameters were extracted and a 3-layers hierarchical fuzzy inference system was developed to detect and alarm the patient’s high-risk behavior without wearable sensors. The results show 98% accuracy in detecting the patient’s activity and 84% accuracy in determining its abnormality.
... For instance, 'Excessive vocalization' cannot be quantified to build CEP numerical pattern rules. To overcome the limitations of the basic CEP rules, we introduce fuzzy logic that handles imprecision and effectively represents psychological knowledge [40,41], extending the basic CEP rules for Level-3 symptomatic complex behavior monitoring. To evaluate the proposed system, we develop a prototype system with real-world datasets that include eight dogs' daily routines and separation anxiety scenarios. ...
... To address this issue, we introduce fuzzy logic and expand the existing CEP rules in the complex behavior EPN. As a common approach to solving imprecise and vague problems, Fuzzy logic has a long history in automated clinical diagnosis [63,64] Moreover, it is easier for experts to map their expertise into fuzzy logic than sophisticated probabilistic methods [40,41]. Figure 5 illustrates the fuzzy logic function structure of the proposed dog monitoring system, with the main steps described as follows: ...
... As a common approach to solving imprecise and vague problems, Fuzzy logic has a long history in automated clinical diagnosis [63,64]. Moreover, it is easier for experts to map their expertise into fuzzy logic than sophisticated probabilistic methods [40,41]. Figure 5 illustrates the fuzzy logic function structure of the proposed dog monitoring system, with the main steps described as follows: ...
an increasing number of people own dogs due to the emotional benefits they bring to their owners. However, many owners are forced to leave their dogs at home alone, increasing the risk of developing psychological disorders such as separation anxiety, typically accompanied by complex behavioral symptoms including excessive vocalization and destructive behavior. Hence, this work proposes a multi-level hierarchical early detection system for psychological Separation Anxiety (SA) symptoms detection that automatically monitors home-alone dogs starting from the most fundamental postures, followed by atomic behaviors, and then detecting separation anxiety-related complex behaviors. Stacked Long Short-Term Memory (LSTM) is utilized at the lowest level to recognize postures using time-series data from wearable sensors. Then, the recognized postures are input into a Complex Event Processing (CEP) engine that relies on knowledge rules employing fuzzy logic (Fuzzy-CEP) for atomic behaviors level and higher complex behaviors level identification. The proposed method is evaluated utilizing data collected from eight dogs recruited based on clinical inclusion criteria. The experimental results show that our system achieves approximately an F1-score of 0.86, proving its efficiency in separation anxiety symptomatic complex behavior monitoring of a home-alone dog.
... Recognising individual activities of people susceptible to hazardous behaviours such as falls, wandering, and agitation has been an active research topic, which has witnessed the use of pervasive and non-pervasive Sensing Solutions (SSs) [1]. Interestingly, many cases of hazardous behaviours in ageing adults can be prevented [2,3]. ...
This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.
... However, early works in the context of smart homes have used fuzzy logic for knowledge-based activity recognition as well. The work of Medjahed et al. [23], for example, used sets of fuzzy if-then rules expressed by experts with vague and imprecise terms for recognizing various Activities of the Daily Living (ADL) like "sleeping", "getting up" or "washing dishes". The recognition was done by fuzzifying information gathered from simulated infrared sensors, state-change sensors, and microphones. ...
