Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring... more
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine (SVM) classifier.
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring... more
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring... more
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.
This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover, it... more
This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover, it works with infra-red on and infra-red off. The method consists of five parts – training image acquisition, background subtraction, feature extraction, system training, and system testing. Firstly, some sample video clips have been taken with an IP camera for initial system implementation. The clips are then filtered to separate background and foreground. After that, some morphological operations are carried out to identify the most significant motion in the foreground. Those parts are cropped with some extra area and used to train a multiclass support vector machine (SVM) along with an image subset of the people detection dataset of The National Institute for Research in Computer Science and Control (French: Institut National de Recherche en Inform...
In the past few years,several works describing systems for the prompt detection of falls have been presented in literature. Many of these systems address the problem of fall detection by using some handcrafted features extracted from the... more
In the past few years,several works describing systems for the prompt detection of falls have been presented in literature. Many of these systems address the problem of fall detection by using some handcrafted features extracted from the input signals.In the meantime interest in the use of feature learning and deep architectures has been increasing, thus reducing the required engineering effort and the need for prior knowledge. A fall detection method based on a Deep Convolutional Neural Network Autoencoder is presented in this work. This method is trained as a novelty detector through the end-to-end strategy. The classifier distinguishes normal sound events generated by common indoor human activity (i.e. footsteps and speech) and music background from novelty sound events produced by human falls. The performance of the algorithm has been assessed on a corpus of fall events created by the authors. Moreover a comparison was made with two different state-of-art algorithms both based on a One Class Support Vector Machine. The results showed an improvement on performance of about 18,2% on average.
— In today's world, surveillance plays an important role in securing property from various suspicious and intrusion activities. In such surveillance, technology plays an important role by making it easy for humans to gather information... more
— In today's world, surveillance plays an important role in securing property from various suspicious and intrusion activities. In such surveillance, technology plays an important role by making it easy for humans to gather information from surroundings at one place by using various electronic devices such as cameras, sensors and various monitoring devices. But search surveillance involves human interceptions to manage and control the system and makes the system dependable. Also to monitor and record the activity it is must to capture every frame and instance of movement. In The following implementation we use Surveillance Camera and Multi-sensor Data Fusion for improving detection, tracking and identification which also improves situation assessment and awareness. Also it provides spatial and temporal coverage, shorter response time and reduces communication and computing.
In-house video surveillance can represent an excellent support for people with some difficulties (e.g. elderly or disabled people) living alone and with a limited autonomy. New hardware technologies and in particular digital cameras are... more
In-house video surveillance can represent an excellent support for people with some difficulties (e.g. elderly or disabled people) living alone and with a limited autonomy. New hardware technologies and in particular digital cameras are now affordable and they have recently gained credit as tools for (semi-) automatically assuring people's safety. In this paper a multi-camera vision system for detecting and tracking people and recognizing dangerous behaviours and events such as a fall is presented. In such a situation a suitable alarm can be sent, e.g. by means of an SMS. A novel technique of warping people's silhouette is proposed to exchange visual information between partially overlapped cameras whenever a camera handover occurs. Finally, a multi-client and multi-threaded transcoding video server delivers live video streams to opera-tors=remote users in order to check the validity of a received alarm. Semantic and event-based transcoding algorithms are used to optimize the bandwidth usage. A two-room setup has been created in our laboratory to test the performance of the overall system and some of the results obtained are reported.
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those... more
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.
Nowadays, the detection of human fall is a problem recognized by the entire scientific community. Methods that have good performance use human falls samples in the train set, while methods that do not use it, can only work well under... more
Nowadays, the detection of human fall is a problem recognized by the entire scientific community. Methods that have good performance use human falls samples in the train set, while methods that do not use it, can only work well under certain conditions. Since examples of human falls are very difficult to retrieve, there is a strong need to develop systems that can work well event with few or no data to be used for their training phase. In this article, we show a first study on few-shot learning Siamese Neural Network applied to human falls detection by using audio signals. This method has been compared with algorithms based on SVM and OCSVM, all evaluated starting from the same conditions. The proposed approach is able to learn the differences between signals belonging to different classes of events. In classification phase, using only one human fall signal as a template, it achieves about 80% of F 1 −Measure related to the human fall class, while the SVM based method gets around 69%, when it is trained in the same data knowledge conditions.
Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for... more
Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for elderly people is due to the consequences of a fall. In this paper, we propose a human fall classification system based on an innovative floor acoustic sensor able to capture the acoustic waves transmitted through the floor. The algorithm employed is able to discriminate human falls from non falls and it is based on Mel-Frequency Cep-stral Coefficients and a two class Support Vector Machine. The dataset employed for performance evaluation is composed by falls of a human mimicking doll, everyday objects and everyday noises. The obtained results show that the proposed solution is suitable for human fall detection in realistic scenarios, allowing to guarantee a 0% miss probability at very low false positive rates.
Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need... more
Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm.
—This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover,... more
—This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover, it works with infra-red on and infra-red off. The method consists of five parts – training image acquisition, background subtraction, feature extraction, system training, and system testing. Firstly, some sample video clips have been taken with an IP camera for initial system implementation. The clips are then filtered to separate background and foreground. After that, some morphological operations are carried out to identify the most significant motion in the foreground. Those parts are cropped with some extra area and used to train a multiclass support vector machine (SVM) along with an image subset of the people detection dataset of The A total of 597 images have been used as positive images and a total of 662 images have been used as negative images. Average detection accuracy of the system without infra-red is 89.37% and average detection accuracy of the system with infra-red is 72.66%. Therefore the average detection accuracy is 81.1%. We conclude (using dependent probabilistic analysis) that our system performs on an average of 89.37% accuracy based on our frame based analysis of video feeds.
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring... more
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.
Many fall detection systems are developed using accelerometer and gyroscope on smartphone. In existing fall detection systems, smartphone location placement is generally selected from the beginning and cannot be repositioned. The movement... more
Many fall detection systems are developed using accelerometer and gyroscope on smartphone. In existing fall detection systems, smartphone location placement is generally selected from the beginning and cannot be repositioned. The movement of smartphone decreases the accuracy of the system. This research provides fall detection model using accelerometer on smartphone and the placement of the smartphone could vary as of six predetermined positions. The method used for fall detection is threshold method applying only one parameter, the value of resultant acceleration. There are two thresholds used in this research, upper threshold and lower threshold. The testing of fall detection model is conducted by eight volunteers (four males and four females), and resulted in 98.1% of accuracy, 96.9% of sensitivity, and 100 specificity.
Falls are considered the main cause of fear and loss of independence among the elderly population and are also a major cause of morbidity, disability and health care utilization. In the majority of fall events external support is... more
Falls are considered the main cause of fear and loss of independence among the elderly population and are also a major cause of morbidity, disability and health care utilization. In the majority of fall events external support is imperative in order to avoid major consequences. Therefore, the ability to automatically detect these fall events could help reducing the response time and significantly improve the prognosis of fall victims. This paper presents a unobtrusive smartphone based fall detection system that uses a combination of information derived from machine learning classification applied in a state machine algorithm. The data from the smartphone built-in accelerometer is continuously screened when the phone is in the user's belt or pocket. Upon the detection of a fall event, the user location is tracked and SMS and email notifications are sent to a set of contacts. The accuracy of the fall detection algorithm here proposed is near 97.5% for both the pocket and belt usage. In conclusion, the proposed solution can reliably detect fall events without disturbing the users with excessive false alarms, presenting also the advantage of not changing the user's routines, since no additional external sensors are required.
Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a controllable field by individuals. Therefore a technology, software products is needed. In this paper we focused on what we have done about... more
Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a controllable field by individuals. Therefore a technology, software products is needed. In this paper we focused on what we have done about crowd analysis and examination the problem of human detection with fish-eye lenses cameras. In order to identify human density, one of the machine learning algorithm, which is Haar Classification algorithm, is used to distinguish human body under different conditions. First, motion analysis is used to search for meaningful data, and then the desired object is detected by the trained classifier. Significant data has been sent to the end user via socket programming and human density analysis is presented.
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring... more
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.
Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need... more
Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm.
Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for... more
Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for elderly people is due to the consequences of a fall. In this paper, we propose a human fall classification system based on an innovative floor acoustic sensor able to capture the acoustic waves transmitted through the floor. The algorithm employed is able to discriminate human falls from non falls and it is based on Mel-Frequency Cepstral Coefficients and a two class Support Vector Machine. The dataset employed for performance evaluation is composed by falls of a human mimicking doll, everyday objects and everyday noises. The obtained results show that the proposed solution is suitable for human fall detection in realistic scenarios, allowing to guarantee a 0% miss probability at very low false positive rates.
In this paper, we present a computer vision based system able to detect human falls. We show in detail all the stages of our system and the considerations taken for the provided results. We propose a simple scheme for detection and... more
In this paper, we present a computer vision based system able to detect human falls. We show in detail all the stages of our system and the considerations taken for the provided results. We propose a simple scheme for detection and tracking followed by a Finite State Machine (FSM). The proposed system presents a good performance under different environment conditions.
