Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

Self Cite

The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.

Section: Methods and Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements

Kan

Kuo

Lin

2019

Self Cite

“…Sarcevic et al [ 31 ] have used a combination of accelerometers, gyroscopes, and magnetometers on both wrists of the subject and achieved a 91.74% activity recognition accuracy on a set of 11 activities. Other experiments have used accelerometers on different parts of the body, such as the right part of the chest and the left thigh [ 32 ], on both wrists and the torso [ 33 ] with added RFID wristband readers in [ 34 ] to monitor human-object interaction. Raad et al [ 35 ] have opted for the use of RFID tags on ankle bracelets for localization and tracking of elders with Alzheimer’s.…”

Section: Offline Activity Recognitionmentioning

confidence: 99%

From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review

Baghezza

Bouchard

Bouzouane

et al. 2021

This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.

“…In this paper, ANFIS technology is used to design a fire detection control system and reduce false alarms. ANFIS technology has been used in mobile robot navigation [16], healthcare monitoring systems [17], air conditioning control [18], flood susceptibility modeling [19], and many other applications. In recent times, fiberoptic sensors were used for structural fire engineering [20], however, there is a need for true fire identification.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fire Warning Application Using IoT and an Adaptive Neuro-Fuzzy Inference System

Sarwar

Bajwa

Jamil

et al. 2019

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user’s smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output.