Yunkun Ning

Pre-impact and Impact Detection of Falls Using Built-In Tri-accelerometer of Smartphone

Falls in elderlies are a major health and economic problem. Research on falls in elderly people has the great social significance under the population aging. Previous smartphone-based fall detection systems have not both fall detection and fall prevention, and the feasibility has not been fully examined. In this paper, we propose a smartphone-based fall detection system using a threshold-based algorithm to distinguish between Activities of Daily Living (ADL) and falls in real time. The smartphone with built-in tri-accelerometer is used for detecting early-warning of fall based on pre-impact phase and post-fall based on impact phase. Eight healthy Asian adult subjects who wear phone at waist were arranged to perform three kinds of daily living activities and three kinds of fall activities. By comparative analysis of threshold levels for acceleration, in order to get the best sensitivity and specificity, acceleration thresholds were determined for early pre-impact alarm (4.5-5m/s2) and post-fall detection (21-28 m/s2) under experimental conditions.

Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms.

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

A wearable pre-impact fall early warning and protection system based on MEMS inertial sensor and GPRS communication

Fall is one of great threats affecting people with old age. Aimed at the falling issue of aged, the paper explored a pre-impact fall early warning and protection system. This system consists of an early fall alarm, protection airbags, a remote monitoring platform and a guardians cellphone app. The early fall alarm and airbags are integrated in a belt, convenient for wearing and hip-protection. The inner of early fall alarm has a MEMS sensor, which collects 3-axis accelerated velocity and 3-axis angular velocity. A fall detection algorithm is applied to recognize falls from activities of daily living (ADL). When there is a dangerous movement approaching fall, the early fall alarm will warn the aged to stop the movement. When the fall happens, the early fall alarm will trigger the airbag system, then the airbags in the belt will inflate as soon as possible to reduce the damage to the aged. In addition, the early fall alarm will ring and send message to the guardians cellphone for help. Meanwhile, the kinematics of the human body during falling time will be stored in TF card and sent to remote monitoring platform for storage. Then the monitoring platform can show the fall location where the fall incident happens in the electronic map. In order to test the reliability of this early fall alarm and protection system, a series of experiments have been designed. The results show that this system can be relatively accurate to detect falls, accomplishing functions including early fall warning and alarming, airbag inflation, statics transferring and storage, real-time location, which has significant benefit for reducing the direct damage and shortening the aiding time.

Exploration and comparison of the pre-impact lead time of active and passive falls based on inertial sensors.

Research on falls in elderly people has a great social significance because of the rapidly growing of the aging population. The pre-impact lead time of fall (PLT) is an important part of the human fall theory. PLT is the longest time for a person who is going to fall to take action in order to prevent the fall or to reduce bodily injuries from the fall impact. However, there is no clear definition of PLT so far. There is also no comparative study for active and passive falls. In this study, we proposed a theoretical definition of the PLT, based on a new method of fall event division. We also compared the differences of PLT and the related angles between active and passive falls. Eight healthy adult subjects were arranged to perform three kinds of activities of daily living (sitting, walking and lying), and two kinds fall activities (active and passive) in three directions (forward, backward and lateral fall). Nine inertial sensor modules were used to measure the body segmental kinematic characteristics of each subject in our experimental activities. In this paper, a fall event was suggested to divide into three or four phases and then the critical phase could be divided into three periods (pre-impact, impact, and post-impact). Two fall models were developed for active and passive falls using acceleration data. The average value of PLT for active falls is about 514 ± 112 ms and it is smaller than the value for passive falls, which is 731 ± 104 ms. The longest PLTs were measured on the chest or waist instead of other locations, such as the thigh and shank. The PLTs of the three kinds of fall activities were slightly different, but there was a significant difference between two fall modes. The PLT showed the correlation to the body angle at the start of PLT, but it was uncorrelated at the end of PLT. The angles at the start of PLT had slight variations (<10 degrees) from the steady standing state except in passive forward falls (max 16 degrees) due to the self-control. The landing angles were significantly different in the both fall modes in all the three directions of fall, indicating the state of the trunk was uncertain when the hip contacted the ground. It can be concluded that it is feasible to prevent falls by using an early pre-impact fall alarm device; the present study provides important reference for development of pre-impact fall alarm devices.

Implementation of Android-based fall-detecting system

Accidental falls are the main factors that endanger the health of elderly people in modern society. Timely and effective fall detection and alarm can reduce the risk of falls. With the increasing of the aging society, the design and development of the fall detection system which is portable, accurate and real-time, has gradually become one of the most urgent needs of the community. With the rapid development of mobile Internet, smart phones as another kind of wearable device have become essential products of peoples life. Smart phone is portable and flexible, which can be built in various sensors. It can be used to monitor human motion data. In order to detect falls promptly and minimize the damage to the aged, a fall detection system based on Android phones was designed and implemented and a fall detection algorithm for phones was proposed in the paper. In this way, the elderly can detect falls without other equipment at any time. Meanwhile, the system integrated the phone system function greatly, provided a good platform for human-computer interaction in the detection, and gave remote alarm to help the elderly get timely rescue.

Accurate Fall Detection Algorithm Based on SBPSO-SVM Classifier

For the purpose of improving the medical care which aims at the elderly and the chronic patients who are prone to falls, this paper makes use of Standard Binary Particle Swarm Optimization(SBPSO) to search for the combination of best feature subset and parameters (C, g), which can be used to train the SVM(Support Vector Machine). Experiments results show that the proposed method can get higher accuracy (about 99%) compared with non-optimized SVM, k-NN (k Nearest Neighbors) and threshold-based method when dealing with the classification of ADL (Activities in Daily Life) and abnormal falls.

Wireless transmission system for motion sensing game controller based on low power Bluetooth technology

Motion sensing game is a novel game and operated by body segmental movements. It is respected by the majority of players of all ages because of its excellent interactivity. At present, many companies adopt the method of video capture to implement the collection of movement information, but the cost is much higher. And it also can achieve the accurate control of the game through the sensing technology of inertial sensor. This paper introduces a kind of wearable motion sensing game device presented in the form of glasses. Using a 9-axis sensor module determines the glasses posture, sequentially realizing real-time detection of the head movement. The inertial data is packaged and sent to the according base-station by the BLE4.0 wireless technology. The base-station contains an interface circuit of USART to USB and can transmit inertial data to the PC through this interface. Compared with using the RF module (e. g nrf24l01), the Bluetooth 4.0 technology has the advantage of low power consumption, low cost, small volume and easy to connect to phone. So this communication technology is suitable for the use in the motion sensing game controller.

Relationship between dynamical characteristics of sit-to-walk motion and physical functions of elderly humans

Sit-to-walk (STW) motion is essential for daily activities. Falls frequently occur, when there is impaired ability to perform STW movements. This study investigated the relationships between the dynamical characteristics of STW motion and physical functions of elderly people. 128 elderly (51 males and 77 females, above 65 years) participated in this study. Participants were instructed to perform STW motion at comfortable state and classified into four groups (normal, mild, moderate and severe group) based on physical function, which evaluated with functional reach test and dynamic gait test. The results showed that some relationships were confirmed between the sample entropy characteristics of STW motion. Moreover, a subset of variables was significant different among four groups via Kruskal-Wallis test, which could be potentially used for developing an objective and simple methods to assess balance capacity and fall risk level of elderly people.Sit-to-walk (STW) motion is essential for daily activities. Falls frequently occur, when there is impaired ability to perform STW movements. This study investigated the relationships between the dynamical characteristics of STW motion and physical functions of elderly people. 128 elderly (51 males and 77 females, above 65 years) participated in this study. Participants were instructed to perform STW motion at comfortable state and classified into four groups (normal, mild, moderate and severe group) based on physical function, which evaluated with functional reach test and dynamic gait test. The results showed that some relationships were confirmed between the sample entropy characteristics of STW motion. Moreover, a subset of variables was significant different among four groups via Kruskal-Wallis test, which could be potentially used for developing an objective and simple methods to assess balance capacity and fall risk level of elderly people.

A quaternion-based Attitude Estimate System Based on a Low Power Consumption Inertial Measurement Unit

Accurate and real-time tracking of the orientation or attitude of rigid bodies has traditional applications in robotics, aerospace, underwater vehicles, human body motion capture, etc. Towards human body motion capture, especially wearable devices, the use of a longer time has always been a challenge for several weeks or several months continuously, so a low-cost chip and a low computational cost algorithm are necessary .The paper presented a quaternion-based algorithm that integrated the sensor output with the Kalman filtering algorithm, and a low power consumption Inertial Measurement Unit (IMU) for the attitude estimation. The low power consumption IMU with an inner Digital Motion Processor(DMP) from InvenSense Inc. called MPU9150, which contains triaxial accelerometers, triaxial gyroscopes, triaxial magnetometers and inner DMP. Firstly, we got attitude quaternion from DMP, and used the factored quaternion algorithm (FQA) to calculate course angle quaternion component. Then the Kalman Filtering algorithm was used to mix them together to acquire the accurate and good real-time performance attitude .The experimental results showed that Kalman filtering algorithm to mix DMP output and magnetometers data have better performance than gradient descent algorithm and complementary filter algorithm even in static performance and dynamic performance and power consumption.