A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System

Abstract

We propose in this paper a novel algorithm as well as architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a fall accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

Soft Fall Detection Using Machine Learning in Wearable Devices

“…We can observe that the Decision Tree Ensemble scores clearly the best and outperform the results obtained by the other algorithms like the MLP cited very often in the litterature [12], [14], [16], [19], [20]. By observing the classification errors made by the MLa it was possible to determine that some activities like climbing down stairs were often detected as soft falls.…”

“…Other research team obtained similar results on hard falls like Kau & al. [14] or Albert & al. [16].…”

New Advances and Challenges of Fall Detection Systems: A Survey

“…It also has an explicit change: accelerometer usually referred to a kind of specific device before 2014, while it mostly referred to the built-in component in the smart phone after 2014. Based on a smart phone and the third generation (3G) networks, Kau et al [19] proposed the fall accident detection and corresponding wide area rescue system. Aguiar et al [18] proposed a fall detection system based on smart phone.…”

Human Activity Recognition Based on Quantization on Feature’s Classification Capability

“…Due to the advantages of no need to deploy in advance, smaller data volume, lower cost and power consumption, sensors-based HAR stands out among various technologies [1][2][3] and has been drawing tremendous attention and applied into a variety application areas, such as medical care [1], emergency rescue [2], and smart home surveillance [3].…”

“…Most related works use data-driven methods which tend to take the advantage of multiple sensors [2,4] as well as improved feature extraction and classification algorithms [3][4][5][6][7][8][9][10] to extend the HAR classification model's generalization performance. However, this may lead to two drawbacks.…”