Hardware/software co-design of fractal features based fall detection system

Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques

Computer vision based posture estimation and fall detection.

Falls are a major health problem, especially in the elderly population. Increasing fall events demands a high quality of service and dedicated medical treatment which is an economic burden. Serious injuries due to fall can cost lives in the absence of immediate care and support. There- fore, a monitoring system that can accurately detect fall events and generate instant alerts for immediate care is extremely necessary. To address this problem, this research aims to develop a computer vision-based fall detection system. This study proposes fall detection in three stages: (A) Detection of human silhouette and recognition of the pose, (B) Detection of the human as three regions for different postures including fall and (C) Recognise fall and non-fall using locations of human body regions as distinguishing features. The first stages of work comprise human silhouette detection and identification of activities in the form of different poses. Identifying a pose is important to understand a fall event where a change of pose defines its characteristics. A fall event comprises of sequential change of poses and ends up in a lying pose. Initial pose during a fall can be standing, sitting or bending but the final pose is usually a lying pose. It would, therefore, be beneficial if lying pose is recognised more accurately than other normal activities such as standing, sitting, bending or crawling to address a fall. Hence in the first stage, Background Subtraction (BS) is used to detect human silhouette. After background subtraction, the foreground images were used in a Convolutional Neural Network (CNN) to recognise different poses. The RGB and the Depth images were captured from a Kinect Sensor. The fusion of RGB and Depth images were explored for feeding to a convolutional neural net- work. Depth together with RGB complimented each other to overcome their weakness respectively and proved to be a significant strategy. The classification was performed using CNN to recognise different activities with 81% accuracy on validation. The other challenge in fall detection is the tracking of a person during a fall. Background Subtraction is not sufficient to track a fallen person especially when there are lighting and viewpoint variations in the environment and present of another object like furniture, a pet or even another person. Furthermore, tracking be- comes tougher during the fall in comparison to normal activities like walking or sitting because the rate of change pose is higher during a fall. To overcome this, the idea is to locate the regions in the body in every frame and consider it as a stable tracking strategy. The location of the body parts provides crucial information to distinguish falls from the other normal activities as the person is detected all the time during these activities. Hence the second stage of this research consists of posture detection using the pose estimation technique. This research proposes to use CNN based pose estimation using simplified human postures. The available joints are grouped according to three regions: Head, Torso and Leg and then finally fed to the CNN model with just three inputs instead of several available joints. This strategy added stability in pose detection and proved to be more effective against complex poses observed during a fall. To train the CNN model, transfer learning technique was used. The model was able to achieve 96.7% accuracy in detecting the three regions on different human postures on the publicly available dataset. A system which considers all the lying poses as falls can also generate a higher false alarm. Lying on bed or sofa can easily generate a fall alarm if they are recognised as falls. Hence, it is important to recognise actual fall by considering a sequence of frames that defines a fall and not just the lying pose. In the third and final stage, this study proposes Long Short-Term Memory (LSTM) recurrent networks-based fall detection. The proposed LSTM model uses the detected three region’s location as input features. LSTM is capable of using contextual information from the sequential input patterns. Therefore, the LSTM model was fed with location features of different postures in a sequence for training. The model was able to learn fall patterns and distinguish them from other activities with 88.33% accuracy. Furthermore, the precision of the fall class was 1.0. This is highly desirable in the case of fall detection as there is no false alarm and this means that the cost incurred in calling medical support for a false alarm can be completely avoided

Human Activity Recognition and Fall Detection Using Unobtrusive Technologies

As the population ages, health issues like injurious falls demand more attention. Wearable devices can be used to detect falls. However, despite their commercial success, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this thesis, a monitoring system consisting of two 24×32 thermal array sensors and a millimetre-wave (mmWave) radar sensor was developed to unobtrusively detect locations and recognise human activities such as sitting, standing, walking, lying, and falling. Data were collected by observing healthy young volunteers simulate ten different scenarios. The optimal installation position of the sensors was initially unknown. Therefore, the sensors were mounted on a side wall, a corner, and on the ceiling of the experimental room to allow performance comparison between these sensor placements. Every thermal frame was converted into an image and a set of features was manually extracted or convolutional neural networks (CNNs) were used to automatically extract features. Applying a CNN model on the infrared stereo dataset to recognise five activities (falling plus lying on the floor, lying in bed, sitting on chair, sitting in bed, standing plus walking), overall average accuracy and F1-score were 97.6%, and 0.935, respectively. The scores for detecting falling plus lying on the floor from the remaining activities were 97.9%, and 0.945, respectively. When using radar technology, the generated point clouds were converted into an occupancy grid and a CNN model was used to automatically extract features, or a set of features was manually extracted. Applying several classifiers on the manually extracted features to detect falling plus lying on the floor from the remaining activities, Random Forest (RF) classifier achieved the best results in overhead position (an accuracy of 92.2%, a recall of 0.881, a precision of 0.805, and an F1-score of 0.841). Additionally, the CNN model achieved the best results (an accuracy of 92.3%, a recall of 0.891, a precision of 0.801, and an F1-score of 0.844), in overhead position and slightly outperformed the RF method. Data fusion was performed at a feature level, combining both infrared and radar technologies, however the benefit was not significant. The proposed system was cost, processing time, and space efficient. The system with further development can be utilised as a real-time fall detection system in aged care facilities or at homes of older people