Fallen People Detection Capabilities Using Assistive Robot

One of the main problems in the elderly population and for people with functional disabilities is falling when they are not supervised. Therefore, there is a need for monitoring systems with fall detection functionality. Mobile robots are a good solution for keeping the person in sight when compared to static-view sensors. Mobile-patrol robots can be used for a group of people and systems are less intrusive than ones based on mobile robots. In this paper, we propose a novel vision-based solution for fall detection based on a mobile-patrol robot that can correct its position in case of doubt. The overall approach can be formulated as an end-to-end solution based on two stages: person detection and fall classification. Deep learning-based computer vision is used for person detection and fall classification is done by using a learning-based Support Vector Machine (SVM) classifier. This approach mainly fulfills the following design requirements—simple to apply, adaptable, high performance, independent of person size, clothes, or the environment, low cost and real-time computing. Important to highlight is the ability to distinguish between a simple resting position and a real fall scene. One of the main contributions of this paper is the input feature vector to the SVM-based classifier. We evaluated the robustness of the approach using a realistic public dataset proposed in this paper called the Fallen Person Dataset (FPDS), with 2062 images and 1072 falls. The results obtained from different experiments indicate that the system has a high success rate in fall classification (precision of 100% and recall of 99.74%). Training the algorithm using our Fallen Person Dataset (FPDS) and testing it with other datasets showed that the algorithm is independent of the camera setup.

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Fall Detection System-Based Posture-Recognition for Indoor Environments

Journal of Imaging ◽

10.3390/jimaging7030042 ◽

2021 ◽

Vol 7 (3) ◽

pp. 42

Author(s):

Abderrazak Iazzi ◽

Mohammed Rziza ◽

Rachid Oulad Haj Thami

Keyword(s):

Detection System ◽

Head Injuries ◽

Fall Detection ◽

The Elderly ◽

Support Vector ◽

Svm Classifier ◽

Indoor Environments ◽

Risk Of Death ◽

Senior Population ◽

Public Datasets

The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.

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Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Sensors ◽

10.3390/s21134335 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4335

Author(s):

Goran Šeketa ◽

Lovro Pavlaković ◽

Dominik Džaja ◽

Igor Lacković ◽

Ratko Magjarević

Keyword(s):

Large Scale ◽

Detection System ◽

Low Cost ◽

Fall Detection ◽

Support Vector ◽

Svm Classifier ◽

Detection Accuracy ◽

Detection Systems ◽

Data Segmentation ◽

Post Impact

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

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An Efficient Machine Learning-based Elderly Fall Detection Algorithm

10.21203/rs.3.rs-39065/v1 ◽

2020 ◽

Author(s):

Faisal Hussain ◽

Muhammad Basit Umair ◽

Muhammad Ehatisham-ul-Haq ◽

Ivan Miguel Pires ◽

Tânia Valente ◽

...

Keyword(s):

Machine Learning ◽

Elderly People ◽

Daily Life ◽

Fall Detection ◽

The Elderly ◽

Detection Algorithm ◽

Support Vector ◽

Svm Classifier ◽

Fall Incident ◽

Efficient Machine

Abstract 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.

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A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

Sensors ◽

10.3390/s21062254 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2254

Author(s):

Francisco Javier González-Cañete ◽

Eduardo Casilari

Keyword(s):

Low Cost ◽

Fall Detection ◽

The Elderly ◽

Point Of View ◽

False Alarms ◽

Battery Life ◽

Area Network ◽

Battery Lifetime ◽

Detection Systems ◽

Operational Aspects

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.

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Hobbit: Providing Fall Detection and Prevention for the Elderly in the Real World

Journal of Robotics ◽

10.1155/2018/1754657 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20 ◽

Cited By ~ 9

Author(s):

Markus Bajones ◽

David Fischinger ◽

Astrid Weiss ◽

Daniel Wolf ◽

Markus Vincze ◽

...

Keyword(s):

Real World ◽

Independent Living ◽

Low Cost ◽

Fall Detection ◽

The Elderly ◽

Field Trials ◽

Lessons Learned ◽

Human Robot Interaction ◽

Service Robot ◽

Emergency Situations

We present the robot developed within the Hobbit project, a socially assistive service robot aiming at the challenge of enabling prolonged independent living of elderly people in their own homes. We present the second prototype (Hobbit PT2) in terms of hardware and functionality improvements following first user studies. Our main contribution lies within the description of all components developed within the Hobbit project, leading to autonomous operation of 371 days during field trials in Austria, Greece, and Sweden. In these field trials, we studied how 18 elderly users (aged 75 years and older) lived with the autonomously interacting service robot over multiple weeks. To the best of our knowledge, this is the first time a multifunctional, low-cost service robot equipped with a manipulator was studied and evaluated for several weeks under real-world conditions. We show that Hobbit’s adaptive approach towards the user increasingly eased the interaction between the users and Hobbit. We provide lessons learned regarding the need for adaptive behavior coordination, support during emergency situations, and clear communication of robotic actions and their consequences for fellow researchers who are developing an autonomous, low-cost service robot designed to interact with their users in domestic contexts. Our trials show the necessity to move out into actual user homes, as only there can we encounter issues such as misinterpretation of actions during unscripted human-robot interaction.

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Machine Learning based Human Fall Detection System

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35394 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 2677-2682

Author(s):

Nishanth P

Keyword(s):

Machine Learning ◽

Detection System ◽

Wearable Sensors ◽

Fall Detection ◽

The Elderly ◽

World Health ◽

Support Vector ◽

Data Output ◽

Health Organization ◽

Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

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A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow

Unmanned Systems ◽

10.1142/s2301385018500097 ◽

2018 ◽

Vol 06 (04) ◽

pp. 267-275

Author(s):

Ajay Shankar ◽

Mayank Vatsa ◽

P. B. Sujit

Keyword(s):

Optical Flow ◽

Autonomous Navigation ◽

Low Cost ◽

Obstacle Detection ◽

Monocular Vision ◽

Support Vector ◽

Svm Classifier ◽

Range Finding ◽

Image Patches ◽

Computational Resources

Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and payload capacity. Further, existing direct range-finding methods have the trade-off of complexity against range. In this paper, we propose a vision-based system for obstacle detection which is lightweight and useful for low-cost robots. Currently, monocular vision approaches used in the literature suffer from various environmental constraints such as texture and color. To mitigate these limitations, a novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles. A support vector machine (SVM) classifier that uses [Formula: see text]-HOOF for real-time obstacle classification is utilized. To avoid obstacles, a behavior-based collision avoidance mechanism is designed that updates the probability of encountering an obstacle while navigating. The proposed approach depends only on the relative motion of the robot with respect to its surroundings, and therefore is suitable for both indoor and outdoor applications and has been validated through simulated and hardware experiments.

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Supervised Expert System for Wearable MEMS Accelerometer-Based Fall Detector

Journal of Sensors ◽

10.1155/2013/254629 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 31

Author(s):

Gabriele Rescio ◽

Alessandro Leone ◽

Pietro Siciliano

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Fall Detection ◽

Polynomial Kernel ◽

Support Vector ◽

Posture Analysis ◽

Mems Accelerometer ◽

Specificity And Sensitivity ◽

Controlled Environments ◽

Wireless Accelerometer

Falling is one of the main causes of trauma, disability, and death among older people. Inertial sensors-based devices are able to detect falls in controlled environments. Often this kind of solution presents poor performances in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a machine-learning scheme for people fall detection, by using a triaxial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people, and to the relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end user. In order to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an one-class support vector machine classifier in a stand-alone PC.

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A new approach for breast abnormality detection based on thermography

Medical Technologies Journal ◽

10.26415/2572-004x-vol2iss3p245-254 ◽

2018 ◽

Vol 2 (3) ◽

pp. 245-254 ◽

Cited By ~ 1

Author(s):

Chebbah Nabil Karim ◽

Ouslim Mohamed ◽

Temmar Ryad

Keyword(s):

Breast Cancer ◽

High Performance ◽

Support Vector ◽

Svm Classifier ◽

Abnormality Detection ◽

Public Database ◽

New Approach ◽

Image Processing Techniques ◽

Breast Abnormality ◽

Processing Techniques

Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates.

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Support-Vector Machine Approach for Robust Fault Diagnosis of Electric Vehicle Permanent Magnet Synchronous Motor

Annual Conference of the PHM Society ◽

10.36001/phmconf.2020.v12i1.1291 ◽

2020 ◽

Vol 12 (1) ◽

pp. 10

Author(s):

Chunheng Zhao ◽

Yi Li ◽

Matthew Wessner ◽

Chinmay Rathod ◽

Pierluigi Pisu

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Permanent Magnet ◽

High Performance ◽

Permanent Magnet Synchronous Motor ◽

Synchronous Motor ◽

Industrial Applications ◽

Operating Conditions ◽

Support Vector ◽

Svm Classifier

Permanent magnet synchronous motor (PMSM) is a leading technology for electric vehicles (EVs) and other high-performance industrial applications. These challenging applications demand robust fault diagnosis schemes, but conventional strategies based on models, system knowledge, and signal transformation have limitations that degrade the agility of diagnosing faults. These methods require extremely detailed design and consideration to remain robust against noise and disturbances in the actual application. Recent advancements in artificial intelligence and machine learning have proven to be promising next-generation solutions for fault diagnosis. In this paper, a support-vector machine (SVM) utilizing sparse representation is developed to perform sensor fault diagnosis of a PMSM. A simulation model of the pertinent PMSM drive system for automotive applications is used to generate a set of labelled training example sets that the SVM uses to determine margins between normal and faulty operating conditions. The PMSM model includes input as a torque reference profile and disturbance as a constant road grade, against both of which faults must be detectable. Even with limited training, the SVM classifier developed in this paper is capable of diagnosing faults with a high degree of accuracy, suggesting that such methods are feasible for the demanding fault diagnosis challenge in PMSM.

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