Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation

Abstract

<jats:p>The demand of devices for safe mobility of blind people is increasing with advancement in wireless communication. Artificial intelligent devices with multiple input and output methods are used for reliable data estimation based on maximum probability. A model of a smart home for safe and robust mobility of blind people has been proposed. Fuzzy logic has been used for simulation. Outputs from the internet of things (IoT) devices comprising sensors and bluetooth are taken as input of the fuzzy controller. Rules have been developed based on the conditions and requirements of the blind person to generate decisions as output. These outputs are communicated through IoT devices to assist the blind person or user for safe movement. The proposed system provides the user with easy navigation and obstacle avoidance.</jats:p>

IoT garment for remote elderly care network

“…As can be observed, the three main systems are Tele-care, Tele-monitoring and Telemedicine. Actual and particular solutions like “Smart Home” (integrating home automation aspects to avoid several rutine activities to be done) and virtual assistants are different evolutions of Tele-care systems [7] , [8] , [9] , [10] . And other solutions like wearable systems (or “Smart Clothing”) are Tele-monitoring evolutions [11] , [12] , [13] .…”

Cognitive and Affective Assessment of Navigation and Mobility Tasks for the Visually Impaired via Electroencephalography and Behavioral Signals

“…The findings hopefully empower the knowledge of how the visually impaired persons are stressed and emotionally affected by SSD navigation and contribute to the development of the intelligent navigation devices, aiming for the VIPs’ safety and well-being. The results of our work can inspire researchers working in the field of IoT devices comprising sensors, antennas, and Bluetooth, which have created navigation rules based on a fuzzy controller [ 69 ], GPS embedded in a stick with voice recognition for obstacles detection [ 70 ], computer vision-based assistants [ 71 ], or assistive systems relying on wearable smart glasses and mobile applications [ 72 ].…”

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

“…Figure 1 shows the structure of neuro-fuzzy classification [18] conducted and integrated with k-Means clustering [19] in this research. A typical fuzzy system can be referenced in [20,21]. As shown in Figure 1, the fuzzifier transforms crisp input values from the present input into fuzzy sets.…”