Smartphone-based Fall Detection System Research Articles

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors.

Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

Development of an Enhanced Threshold-Based Fall Detection System Using Smartphones With Built-In Accelerometers

Falls are a primary accident for elderly people living independently. Obviously, timely and accurate fall detection is critical to reduce the injuries and avoid the loss of life. In order to improve existing smartphone-based fall detection systems, this paper investigates the features of triaxial acceleration values acquired from built-in accelerometers of a smartphone, identifies crucial thresholds of the falls and non-falls, and then proposes an enhanced threshold-based fall detection approach, which could not only distinguish fall events from the most of daily activities (including walking, running, and sitting down), but also support four directions (forward, backward, left lateral, and right lateral) of the falls. In addition, once a falling accident is identified, the user position would be instantaneously transmitted to an emergency center in order to have timely medical assistance. As a consequence, experimental results show the feasibility of our enhanced approach with accuracy and detection rates of 99.38% and 96%, respectively, while a set of 650 test activities including 11 different kinds of daily activities are performed.

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.

Improving the performance of fall detection systems through walk recognition

Social problems associated with falls of elderly citizens are becoming increasingly important because of the continuous growth of aging population. Automatic fall detection systems represent a possible answer to some of these problems, as they are useful to obtain help in case of serious injuries and to reduce the long-lie problem. Nevertheless, widespread adoption of these systems is strongly influenced by their usability and trustworthiness, which are at the moment not excellent. In fact, the user is forced to wear the device according to placement and orientation restrictions that depend on the considered fall-recognition technique. Also, the number of false alarms generated is too high to be acceptable in real world scenarios. This paper presents a technique, based on walk recognition, that increases significantly both usability and trustworthiness of a smartphone-based fall detection system. In particular, the proposed technique automatically and dynamically determines the orientation of the device, thus relieving the user from the burden of wearing the device with predefined orientation. Orientation is then used to infer posture and eliminate a large fraction of false alarms (98 %).

A multi-sensor approach for fall risk prediction and prevention in elderly

Scientific research on smartphone-based fall detection systems has recently been stimulated due to the growing elderly population and their risk of falls. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to predict and prevent them from happening in the first place. To address the issue of fall prevention, in this paper, we propose a fall prediction system by integrating the sensor data of smartphones with a smartshoe. In our previous research, we designed and implemented a pair of sensing shoes (smartshoe) that contained four pressure sensors with a Wi-Fi communication module in each shoe to unobtrusively collect data in any environment. After assimilating the smartshoe and smartphone sensor data, we performed an extensive set of experiments in the lab environment to evaluate normal and abnormal walking patterns. In the smartphone, the system can generate an alert message to warn the user about the high-risk gait patterns and potentially save them from a forthcoming fall. We validated our approach using a decision tree with 10-fold cross validation and found 97.2% accuracy in gait abnormality detection.

A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers. To reduce the problem of false alarms, the system includes novel techniques for the recognition of those activities of daily living that could be erroneously mis-detected as falls (such as sitting on a sofa or lying on a bed). To limit the intrusiveness of the system, a small external sensing unit can also be used for the acquisition of movement data.