Fall detection system via smart phone and send people location

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Thakur

Han

2021

“…This device used a couple of IoT-based off-the-shelf products that worked in coordination with a microcontroller circuit to detect falls from human motion data. Mousavi et al [29] used acceleration data available from smartphones to develop a fall detection system. This system consisted of an SVM classifier that interacted with the triaxial accelerometer data coming from a smartphone that had an IOS operating system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some of the frameworks that have focused on fall detection are dependent on a specific operating system or platform or device. These include the smartphone-based fall detection approach proposed in [23] that uses an Android operating system, the work presented in [29] that uses an IOS operating system, the methodology proposed in [26] that requires a smart cane, and the approach in [15] that requires a handheld device. To address universal diversity and ensure the wide-scale user acceptance of such technologies, it is important that such fall detection systems are platform-independent and can run seamlessly on any device that uses any kind of operating system.…”

mentioning

confidence: 99%

An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

Thakur

Han

2021

This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human–computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes.

“…Shahiduzzaman [23] used smart helmets to integrate wearable cameras, accelerometers, and gyroscope sensors and processed multi-sensor collaboration data on the edge. Seyed Amirhossein Mousavi [24] proposed a method of using smartphones and acceleration signals to detect falls by using smartphone sensors and reporting the person's position, with an accuracy rate of 96.33%. Kimaya Desai [12] performed human fall detection by deploying a simple 32-bit microcontroller on the wearable belt.…”

Section: Related Workmentioning

confidence: 99%

Fall Detection with CNN-Casual LSTM Network

Wang

Zhan

et al. 2021

Falls are one of the main causes of elderly injuries. If the faller can be found in time, further injury can be effectively avoided. In order to protect personal privacy and improve the accuracy of fall detection, this paper proposes a fall detection algorithm using the CNN-Casual LSTM network based on three-axis acceleration and three-axis rotation angular velocity sensors. The neural network in this system includes an encoding layer, a decoding layer, and a ResNet18 classifier. Furthermore, the encoding layer includes three layers of CNN and three layers of Casual LSTM. The decoding layer includes three layers of deconvolution and three layers of Casual LSTM. The decoding layer maps spatio-temporal information to a hidden variable output that is more conducive relative to the work of the classification network, which is classified by ResNet18. Moreover, we used the public data set SisFall to evaluate the performance of the algorithm. The results of the experiments show that the algorithm has high accuracy up to 99.79%.