scholarly journals Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 72
Author(s):  
Dimitri Kraft ◽  
Karthik Srinivasan ◽  
Gerald Bieber
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Recognition Rate ◽  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithms ◽  
Independent Life ◽  
Iot Devices ◽  
Serious Injuries

A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are developed, but the accuracy and precision are not standardized, comparable, or sometimes even known. In this work, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly. Furthermore, we give an outlook for a convenient application and wrist device.

scholarly journals A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring

2021 ◽  
pp. 18-31
Author(s):  
Gopinath Gopinath ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Elderly Care ◽  
Recognition Rate ◽  
Model Performance ◽  
Fall Detection ◽  
The Elderly ◽  
Severe Injuries ◽  
Independent Life ◽  
Early Fall ◽  
Post Traumatic

A fall of an older adult often leads to severe injuries and is found to be a significant reason for the death due to post-traumatic complications. Many falls happen in the home atmosphere and prevail unrecognized. Thus, the need for reliable early fall detection is necessary for fast help. Lately, the emergence of wearables, smartphones, IoT, etc., made it possible to develop systems fall detection which aids in the remote monitoring of the elderly. The goal is to allow intelligent algorithms and smartphones to detect falls for elderly care and to monitor them regularly. This work presents the Artificial Intelligence of Things for Fall Detection (AIOTFD) system using a slime mould algorithm (SMA) to optimize the final data. The features extracted using SqueezeNet further CNN based SMA used for data optimization. The validation of the AIOTFD model performance is evaluated through the Multiple Cameras Fall Dataset (MCFD) and UR Fall Detection dataset (URFD). The empirical results accentuated the assuring realization of the model compared to other state-of the art methods.The obtained results shows our proposed AIOTFD attains accuracy of 99.82% and 99.79% and databases can be used for additional investigation and optimizations to increase the recognition rate to enhance the independent life of the elderly.

Directional Human Fall Recognition Using a Pair of Accelerometer and Gyroscope Sensors

2011 ◽  
Vol 135-136 ◽  
pp. 449-454
Author(s):  
Myeong Jun Lim ◽  
Jin Ho Cho ◽  
Young Sun Cho ◽  
Tae Seong Kim
Keyword(s):  
Recognition Rate ◽  
Joint Inflammation ◽  
Severe Case ◽  
Fall Detection ◽  
The Elderly ◽  
Recognition Algorithm ◽  
Neural Nets ◽  
Linear Discriminant ◽  
Detection Algorithms ◽  
Ar Modeling

Human fall in the elderly population is one of the major causes of injury or bone fracture: it can be a cause of various injuries (e.g., fracture, concussion, and joint inflammation). It also could be a possible cause of death in a severe case. To detect human fall, various fall detection algorithms have been devised. Most fall detection algorithms rely on signals from a single accelerometer or gyroscope and use a threshold-based method to detect the human fall. However, these algorithms need careful adjustment of a threshold for each subject and cannot detect the direction of falls. In this study, we propose a novel fall recognition algorithm using a pair of a tri-axial accelerometer and a tri-axial gyroscope. Our fall recognition algorithm utilizes a set of augmented features including autoregressive (AR) modeling coefficients of signals, signal magnitude area (SMA), and gradients of angles from the sensors. After Linear Discriminant Analysis (LDA) of the augmented features, an Artificial Neural Nets (ANNs) is utilized to recognize four directional human falls: namely forward fall, backward fall, right-side fall, and left-side fall. Our recognition results show the mean recognition rate of 95.8%. Our proposed fall recognition technique should be useful in the investigation of fall-related injuries and possibly in the prevention of falls for the elderly.

scholarly journals AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1889
Author(s):  
Francisco Luna-Perejón ◽  
Luis Muñoz-Saavedra ◽  
Javier Civit-Masot ◽  
Anton Civit ◽  
Manuel Domínguez-Morales
Keyword(s):  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Life Activities ◽  
Learning Classifier ◽  
The People ◽  
Activity Types ◽  
Remote Monitoring Systems ◽  
Secondary Study

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.

scholarly journals Design of a seniors and Alzheimer's disease caring service platform

2021 ◽  
Vol 21 (S10) ◽  
Author(s):  
Cheng-Wen Lee ◽  
Hsiu-Mang Chuang
Keyword(s):  
Care Service ◽  
Fall Detection ◽  
The Elderly ◽  
Service Platform ◽  
Care Services ◽  
Basic Model ◽  
Dementia Patients ◽  
Mobile Phone Applications ◽  
Iot Devices ◽  
And Behavior

Abstract Background To meet the needs of aging and dementia patients in Taiwan, this study designed a nursing system that includes communication, location tracking, and fall detection, and early warning services. The main purpose of this research is to provide timely services to the elderly and patients and hope to reduce the burden when the number of nursing staff decreases. This article is a remote disease care service platform with the Internet of Things (IoT) devices to monitor the location of the elderly and whether they have dropped warning alerts. Results The device is connected to the patient's waist and chest, monitors the patient's movement and behavior, and transmits messages to the back-end system, and informs caregivers through mobile phone applications when unexpected or shocking events occur. The system can identify whether the patient has fallen, accidentally, or long-term inactivity. The device is equipped with sensors that enable it to monitor the patient's location and behavior data through Bluetooth and GPS technology. Finally, we proposed a basic model and an integrated model that will industrialize the system and is expected to play a role in a larger patient population. Conclusions The system developed in this research has passed the Activities of Daily Living (ADL) test and verification, and is expected to provide appropriate safety care services for nursing homes and elderly residences.

Fall Behavior Recognition Based on Deep Learning and Image Processing

2018 ◽  
Vol 9 (4) ◽  
pp. 1-15
Author(s):  
He Xu ◽  
Leixian Shen ◽  
Qingyun Zhang ◽  
Guoxu Cao
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
False Positive Rate ◽  
Fall Detection ◽  
The Elderly ◽  
Training Data ◽  
Local Network ◽  
Raspberry Pi ◽  
Behavior Recognition ◽  
Risk Of Death

Accidental fall detection for the elderly who live alone can minimize the risk of death and injuries. In this article, we present a new fall detection method based on "deep learning and image, where a human body recognition model-DeeperCut is used. First, a camera is used to get the detection source data, and then the video is split into images which can be input into DeeperCut model. The human key point data in the output map and the label of the pictures are used as training data to input into the fall detection neural network. The output model then judges the fall of the subsequent pictures. In addition, the fall detection system is designed and implemented with using Raspberry Pi hardware in a local network environment. The presented method obtains a 100% fall detection rate in the experimental environment. The false positive rate on the test set is around 1.95% which is very low and can be ignored because this will be checked by using SMS, WeChat and other SNS tools to confirm falls. Experimental results show that the proposed fall behavior recognition is effective and feasible to be deployed in home environment.

scholarly journals Deep Learning-Based Character Gesture Recognition

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Husam Al-Behadili ◽  
Alaa H. Ahmed ◽  
Hasan M.A. Kadhim
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Kinect Sensor ◽  
Accuracy Rate ◽  
Hand Detection ◽  
Detection Algorithms ◽  
Text Input ◽  
Input Method ◽  
Single Sensor

The article describes a new text input method based on gesture recognition, which enables direct physical-to-digital text input. This enables hand-free and in-air writing without any need for keyboards, mice, etc. This is done with the help of state-of-the-art deep learning methods and a single Kinect sensor. The authors were able to achieve a high-accuracy recognition rate by using any wearable device, in contrast to the existing methods, and utilizing a single sensor. Furthermore, among several existing deep learning structures, the authors determined that the best deep learning structure suitable for the character-based gesture data is the DenseNet Convolutional neural network. For instance, the training loss curve shows that DenseNet has the fastest converging curve compared to the others despite maintaining the highest accuracy rate. Our proposed method allows for the improvement of the recognition rate from 96.6% (in the existing algorithms) to 98.01% when the DenseNet structure is used despite using only a single sensor instead of multiple cameras. The use of the Kinect sensor not only reduces the number of cameras to one but also overrides the necessity for any additional hand detection algorithms. These results aid in improving the speed and the efficiency of the character-based gesture recognition. The proposed system can be used in applications that require accurate decision making, such as in operation rooms.

scholarly journals Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

2020 ◽  
Vol 17 (2) ◽  
pp. 408 ◽  
Author(s):  
Grigorios Kyriakopoulos ◽  
Stamatios Ntanos ◽  
Theodoros Anagnostopoulos ◽  
Nikolaos Tsotsolas ◽  
Ioannis Salmon ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Synthetic Data ◽  
Smart Homes ◽  
The Elderly ◽  
Classification Methods ◽  
Inference Models ◽  
Fall Incident ◽  
Temporal Inference ◽  
Iot Devices ◽  
Serious Injuries

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

scholarly journals NT-FDS—A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2006
Author(s):  
Marvi Waheed ◽  
Hammad Afzal ◽  
Khawir Mehmood
Keyword(s):  
Deep Learning ◽  
Missing Values ◽  
Short Term Memory ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
Benchmark Datasets ◽  
Noise Tolerant ◽  
Local Interference

Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets—SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.

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