Recognizing Human Activities and Detecting Falls in Real-time (original) (raw)

Efficient Activity Recognition and Fall Detection Using Accelerometers

Communications in Computer and Information Science, 2013

Ambient assisted living (AAL) systems need to understand the user's situation, which makes activity recognition an important component. Falls are one of the most critical problems of the elderly, so AAL systems often incorporate fall detection. We present an activity recognition (AR) and fall detection (FD) system aiming to provide robust real-time performance. It uses two wearable accelerometers, since this is probably the most mature technology for such purpose. For the AR, we developed an architecture that combines rules to recognize postures, which ensures that the behavior of the system is predictable and robust, and classifiers trained with machine learning algorithms, which provide maximum accuracy in the cases that cannot be handled by the rules. For the FD, rules are used that take into account high accelerations associated with falls and the recognized horizontal orientation (e.g., falling is often followed by lying). The system was tested on a dataset containing a wide range of activities, two different types of falls and two events easily mistaken for falls. The Fmeasure of the AR was 99 %, even though it was never tested on the same persons it was trained on. The F-measure of the FD was 78 % due to the difficulty of the events to be recognized and the need for real-time performance, which made it impossible to rely on the recognition of long lying after a fall.

Evaluating Wearable Activity Recognition and Fall Detection Systems

IFMBE Proceedings, 2015

Activity recognition (AR) and fall detection (FD) research areas are very related in assistance scenarios but evolve independently. Evaluate them is not trivial and the lack of FD real-world datasets implies a big issue. A protocol that fuses AR and FD is proposed to achieve a large, open and growing dataset that could, potentially, provide an enhanced understanding of the activities and fall process and the information needed to design and evaluate high-performance systems.

Human Activity Recognition and Fall Detection

International Journal of Engineering Applied Sciences and Technology

Personalized monitoring and its application is increasing with the advancement of technology. And during pandemics its become very essential to keep an eye on the prone area and one of the area to identify was old age home. Dizziness, unconsciousness, and others are the common problems associated with elderly people due to weakness and this was also the symptoms of covid. So an unusual activity of falling of elderly people was very difficult to identify and also to monitor. The technology was updated till now to identify posture of normal activity such as running, walking, jumping and many but revert to that falling was an area need to explore. During the fall of an elderly person, the injuries are very fatal, and to void this case we proposed a design to identify the fall and try to notify the system about its fall. Although we try to predict the fall so that it becomes easy to monitor and provide medical help as soon as possible. The main theme is to identify the posture activity ...

Activity-Aware Fall Detection and Recognition Based on Wearable Sensors

IEEE Sensors Journal, 2019

The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become signi cant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for e cient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method e ciently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.

Context-based fall detection and activity recognition using inertial and location sensors

Accidental falls are some of the most common sources of injury among the elderly. A fall is particularly critical when the elderly person is injured and cannot call for help. This problem is addressed by many fall-detection systems, but they often focus on isolated falls under restricted conditions, not paying enough attention to complex, real-life situations. To achieve robust performance in real life, a combination of body-worn inertial and location sensors for fall detection is studied in this paper. A novel context-based method that exploits the information from the both types of sensors is designed. It considers body accelerations, location and elementary activities to detect a fall. The recognition of the activities is of great importance and also is the most demanding of the three, thus it is treated as a separate task. The evaluation is performed on a real-life scenario, including fast falls, slow falls and fall-like situations that are difficult to distinguish from falls. All possible combinations of six inertial and four location sensors are tested. The results show that: (i) context-based reasoning significantly improves the performance; (ii) a combination of two types of sensors in a single physical sensor enclosure is the best practical solution.

Fall classification by machine learning using mobile phones

PloS one, 2012

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls-left and right lateral, forward trips, and backward slips-while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.

Evaluation of Human Activity Recognition and Fall detection and Using Android Phone

Human Activity Recognition (AR) using kinematic sensors is one of the widely used and hot researched topic based on the popularity of android phone. Development in sensor networks technology provided birth to the applications that can give intelligent and amicable services based on AR of the people. Despite the fact that the technology supports checking the activities pattern, empowering applications to identify the activities performed independently is still a fundamental concern. For the improvement of quality of life and personal satisfaction, caregiving process can be enhanced by introducing the automatic fall detection, AR, and prevention systems. Modern smartphones have different built in sensors; accelerometer, magnetometer, proximity, and gyroscope which can be used for AR as well as fall detection. In this paper, we present an AR and fall detection framework based on built in sensors with alarm notifications to the concerned person for prompt aid. Signal Magnitude Vector (SMV) algorithm is used to analyze the magnitude of the peaks. To overcome the false alarm activation problem, we introduce a system which uses different threshold levels to determine the daily life activities; walking, standing, and siting, that could be wrongly detected. For assessment, a trial setup is done to acquire sensor’s information of diverse positions.

Human Fall Detection Using Machine Learning Methods: A Survey

International Journal of Mathematical, Engineering and Management Sciences

Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.