Verification of Fall Detection Sensor

Falls are the main cause of injury in the elderly people. Therefore, fall detection system in the elderly is an essential technology for aging society. This project was designed to develop the fall detection system based on wearable sensors and Internet of Things for the real-time fall detection, data storage, calculation, and notification. The raw data, kinematics signal from the daily living of elder, including three-dimensional accelerometer and gyroscope are provided by GY-85, which have been sent bidots cloud via NodeMCU-ESP32. Further, the NodeMCU-ESP32 avail of the raw data to specify the fall detection algorithm. Moreover, the NodeMCU-ESP32 is designed to achieve the seamless communication between the fall detection device and system to acquire and process the data anywhere anytime as long as there is wifi connection. Consequently, the Ubidots cloud is designed to perform the calculation based on support vector machine (SVM) theory. It also sends the fall detection result via LINE notification (chat application) to caregivers. The tests of the proposed system are performed based on activities of daily living and the directions that fall can occur. The results of the testing show that the proposed system can provide high accuracy and reliability in fall detection.

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer to detect impacts and monitor posture. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested by two teams of 5 elderly subjects who wore the sensor system in turn for 2 week each and were monitored for 8 hours a day. The system previously achieved sensitivity of >90% and a specificity of >99%, using young healthy subjects performing falls and normal activities of daily living (ADL). In this study, over 833 hours of monitoring was performed over the course of the four weeks from the elderly subjects, during normal daily activity. In this time no actual falls were recorded, however the system registered a total of the 42 fall-alerts however only 9 were received at the care taker site. A fall detection system incorporated into a custom designed garment has been developed which will help reduce the incidence of the long-lie, when falls occur in the elderly population. However further development is required to reduce the number of false-positives and improve the transmission of messages.

Elderly falls are a growing phenomenon observed within the world. According to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed Machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the current state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.KeywordsFall detectionElderly fall detectionMachine learning algorithmsWearable fall detection systemSisFall datasetMobiFall datasetK-nearest neighborsKNNSupport vector machinesSVMDecision treeRandom forestLogistic regressionNaïve Bayes

A first-aid system is proposed for monitoring elderly patients who risk falling based on fall detection system (FDs) and for providing the first aid kit from an emergency call centre (ECC) to them by using an unmanned aerial vehicle (UAV). In previous studies, the measurement accuracy of falls and heart rate was not sufficiently accurate. In addition, determination of UAV delivery time is also crucial and can save lives. In this work, the proposed system aims to determine patient falls and accurately measure heart rate and reduce UAV delivery time to patients. The FDs was practically implemented based on heartbeat and accelerometer sensors, a microcontroller, a GSM module, and GPS. While, a UAV, a first aid kit, and Smartphone were adopted in an ECC. Falls were accurately detected based on elderly fall detection (EFD) algorithm. The result showed that the proposed FDs was succeeded in monitoring elderly patients’ vital signs with a fall detection accuracy of 99.11%. In addition, the UAV succeeded in all missions and arrived at the patient’s locations before the ambulance in urban areas, with an average time saving of 1.75 min. The proposed elderly first aid system outperformed previous systems presented in terms of fall detection and UAV arrival time.

Falls among older people remain a very important public healthcare issue. Every year over 11 million falls are registered in the U.S. alone. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The final decision of a fall event is taken based on the location of the user. To the best of our knowledge, this is the first fall detection system which works on an independent smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By analyzing real data of activities of daily life of elderly people, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account the residual movement and the location of the user to increase the fall detection accuracy. By testing with real data we have a fall detection system ready to be deployed on the market.

This paper defines two major data sets 1) from wearable inertial measurement sensors and 2) wearable ECG SHIMMER™ sensors. The first dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable SHIMMER™ sensors unit during research studies “Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data” [2] and “A novel fall detection algorithm for elderly using SHIMMER wearable sensors” [3]. The SHIMMER inertial sensor is a lightweight sensing device, incorporated with tri-axial accelerometer, a tri-axial gyroscope and tri-axial magnetometer, mounted on the waist of the subjects. The second dataset is developed to assess the feasibility of using SHIMMER™ wearable third generation ECG sensors for identification of basic heart anomalies by remote ECG analysis. The experimental protocol was carried out according to the Timed Up and Go (TUG) test [1], which is mainly used in fall detection and fall risk assessment systems specially designed for elderly. Three daily life activities such as standing still, walking and sitting on chair and getup were performed along with fall activity in controlled environment. This dataset is available on Data in Brief Dataverse [4] and a data repository [5].

A fall detection system is vital for the safety of older people, as it contacts emergency services when it detects a person has fallen. There have been various approaches to detect falls, such as using a single tri-axial accelerometer to detect falls or fixing sensors on the walls of a room to detect falls in a particular area. These approaches have two major drawbacks: either (i) they use a single sensor, which is insufficient to detect falls, or (ii) they are attached to a wall that does not detect a person falling outside its region. Hence, to provide a robust method for detecting falls, the proposed approach uses three different sensors for fall detection, which are placed at five different locations on the subject’s body to gather the data used for training purposes. The UMAFall dataset is used to attain sensor readings to train the models for fall detection. Five models are trained corresponding to the five sensors models, and a majority voting classifier is used to determine the output. Accuracy of 93.5%, 93.5%, 97.2%, 94.6%, and 93.1% is achieved on each of the five sensors models, and 92.54% is the overall accuracy achieved by the majority voting classifier. The XAI technique called LIME is incorporated into the system in order to explain the model’s outputs and improve the model’s interpretability.

This project aims to design and implement a fall detection system for the elders using machine learning techniques and Internet-of-Things (IoT). The main issue with fall detection systems is false alarms and hence incorporating machine learning in the fall detection algorithm can tackle this problem. Therefore, choosing the right machine learning algorithm for the given problem is essential and several factors need to be considered in making that choice. For this project, the XGBoost algorithm is used and the machine learning model is trained on the Sisfall dataset. A wearable device that is worn on the waist is designed using an accelerometer, a microcontroller, a Global Positioning System (GPS) module and a buzzer. The acceleration data obtained is converted into features and fed into the machine learning model which will then make a prediction. If a fall event has occurred, the buzzer is activated and emergency contacts of the victim are notified immediately using IoT and Global System for Mobile Communications (GSM). This allows the fall victim to be attended quickly, thus reducing the negative consequences of the fall. The details of the fall are stored on the cloud so that they can be easily accessed by healthcare professionals. Testing the system concluded that the XGBoost machine learning algorithm is well suited for this problem due to the small percentage error obtained.

In this paper, a low resolution privacy preserved infrared array sensor is adopted for the applications of the elderly tracking and fall detection. The sensor is composed of a 16 × 4 thermopile array with the corresponding 60° × 16.4° field of view. Each pixel or thermopile element of infrared sensor contains the temperature value. Two infrared sensors are attached to the wall at different places in our system for capturing the three dimensional image information. The foreground of human body is determined by subtracting the image with the background model using the temperature difference characteristic. Using the foreground temperature, the angle of arrival (AOA) from each sensor is obtained. The location is estimated by the AOA based positioning algorithm. The estimated position is passed to the regression model to reduce the positioning error. As a result, the mean error of our tracking algorithm is 13.39 cm. On the other hand, the fall detection algorithm is implemented by extracting the features from the falling action. Two sensors capture the action at the same time. The sensor with larger foreground region is chosen for the feature extraction process. The extracted features are applied to the k-nearest neighbor (k-NN) classification model for the fall detection. In experiment, 80 fall actions and 80 normal actions are collected. Finally, 95.25% sensitivity, 90.75% specificity and 93% accuracy are achieved.

Falls are critical events among elderly people that requires timely rescue. In this paper, we propose a fall detection system consisting of an inertial unit that includes triaxial accelerometer, gyroscope, and magnetometer with efficient data fusion and fall detection algorithms. Starting from the raw data, the implemented orientation filter provides the correct orientation of the subject in terms of yaw, pitch, and roll angles. The system is tested according to experimental protocols, engaging volunteers who performed simulated falls, simulated falls with recovery, and activities of daily living. By placing our wearable sensor on the waist of the subject, the unit is able to achieve fall detection performance above those of similar systems proposed in literature. The results obtained through commonly adopted protocols show excellent accuracy, sensitivity and specificity, improving the results of other techniques proposed in the literature.

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

The lack of fall data for the elderly is a challenging problem in the fall detection community. To date, the fall and activities of daily life simulated by young people have been used in most studies to train and test fall detection algorithms. However, there are differences in movement patterns between young and elderly individuals due to bone aging, which leads to the degradation of the algorithm performance in the elderly population. To solve the above issue, this paper proposes a fall detection algorithm combining Federated Learning and Extreme Learning Machine (Fed-ELM). First, the online extreme learning machine can use a small amount of misclassified user data to update the parameters so that its performance is improved for individual users. Then, Federated Learning is applied to share data information among different users without involving user privacy. In this way, the generalizability of the fall detection algorithm is improved. The performance of the proposed algorithm in different age groups is analyzed by experiments. For young people, the accuracy, sensitivity and specificity reach 96.96%, 94.50% and 99.29%, respectively, and the accuracy on each individual is more than 94%. For elderly individuals, the accuracy, sensitivity and specificity reach 99.07%, 96.00% and 98.33%, respectively, and the accuracy of each individual is more than 96%.

Since threshold-based fall detection has been widely studied by many research groups, accuracy is still a main limitation affected by personal factors. To this end, a personalized threshold extraction approach being adapted for the fall detection usage for different individual is proposed to increase the fall detection accuracy. Moreover, we also implement a fall detection algorithm called ALARM to verify the feasibility of the proposed threshold extraction approach. Results of comprehensive evaluation show it has high accuracy of 96.76% for fall detection, while the sensitivity and the specificity are 92.01% and 99.13%, respectively, based on the data collected from 8 volunteers.

Human fall detection is a critical research area focused on developing methods and systems that can automatically detect and recognize falls, particularly among the elderly and individuals with disabilities. Falls are a major cause of injuries and deaths among these populations, and timely intervention can reduce the severity of consequences. This article presents a comprehensive review of fall detection systems, emphasizing the use of cutting-edge technologies such as deep learning, sensor fusion, and machine learning. The research explores a variety of methodologies and strategies employed in fall detection systems, including the integration of wearable sensors, smartphones, and cameras. By examining various fall detection techniques and their experimental results, the article highlights the effectiveness of these systems in identifying and classifying falls. The study also addresses the challenges and limitations associated with fall detection systems, emphasizing the need for ongoing research and advancements. In summary, this research contributes to the development of advanced fall detection systems, demonstrating their potential to improve the quality of life for the elderly, alleviate healthcare burdens, and provide reliable solutions for fall detection and classification.

Elderly suffers from injuries or disabilities through falls every year. With a high likelihood of falls causing serious injury or death, falling can be extremely dangerous, especially when the victim is home-alone and is unable to seek timely medical assistance. Our fall detection systems aims to solve this problem by automatically detecting falls and notify healthcare services or the victim’s caregivers so as to provide help. In this paper, development of a fall detection system based on Kinect sensor is introduced. Current fall detection algorithms were surveyed and we developed a novel posture recognition algorithm to improve the specificity of the system. Data obtained through trial testing with human subjects showed a 26.5% increase in fall detection compared to control algorithms. With our novel detection algorithm, the system conducted in a simulated ward scenario can achieve up to 90% fall detection rate.KeywordsFall DetectionKinectPosture Recognition