Smart wearables collect and analyze data, and in some scenarios make a smart decision and provide a response to the user and are finding more and more applications in our daily life. In this paper, we comprehensively survey the most recent and important research works conducted in the area of wearable Internet of Things (IoT) and classify the wearables into four major clusters: (i) health, (ii) sports and daily activity, (iii) tracking and localization, and (iv) safety. The fundamental differences of the algorithms associated within each cluster are grouped and analyzed and the research challenges and open issues in each cluster are discussed. This survey reveals that although Cellular IoT (CIoT) has many advantages and can bring enormous applications to IoT wearables, it has been rarely studied by the researchers. This article also addresses the opportunities and challenges related to implementing CIoT-enabled wearables.

Wearables and the Internet of Things (IoT), Applications, Opportunities, and Challenges: A Survey

Falls are abnormal activity events that occur infrequently; however, they are serious health problems among elderly individuals. With the advancements of technologies, falls have been widely studied by scientific researchers to minimize serious consequences and negative impacts. Fall detection and fall prevention are two strategies to tackle fall issues with a variety of sensing techniques and classifier models. Currently, many reviews on fall-related technologies have been presented and analyzed; however, most of them give surveys on the subfield of fall-related systems, while others are not extensive and comprehensive reviews. In fact, the latest researches have a new trend of fusion-based methods to improve the performance of the fall-related systems based on a combination of different sensors or classifier models. Adaptive threshold and radio frequency-based systems are also researched and proposed recently, which are seldom mentioned in other reviews. Therefore, a global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall-related systems are conducted in this paper. Several research challenges and issues in the fall-related field are also discussed and analyzed. The objective of this review paper is to conclude and provide a good position of current fall-related studies to inspire researchers in this field.

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Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

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Elderly Fall Detection Systems: A Literature Survey

Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.

Fall detection system for elderly people using IoT and ensemble machine learning algorithm

Each year, more than 30% of people over 65 years-old suffer some fall Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff Also, we analyze the performance of three recurrent neural networks for fall detections: long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM As many learning algorithms, we have performed a training phase using different test subjects After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU Information obtained using this system did not compromise the user’s privacy, which constitutes an additional advantage of this alternative

A Novel Monitoring System for Fall Detection in Older People

Automatic human fall detection is one important research topic in caring for vulnerable people, such as elders at home and patients in medical places. Over the past decade, numerous methods aiming at solving the problem were proposed. However, the existing methods only focus on detecting human themselves and cannot work effectively in complicated environments, especially for the falls on furniture. To alleviate this problem, a new method for human fall detection on furniture using scene analysis based on deep learning and activity characteristics is presented in this paper. The proposed method first performs scene analysis using a deep learning method faster R-CNN to detect human and furniture. Meanwhile, the space relation between human and furniture is detected. The activity characteristics of the detected people, such as human shape aspect ratio, centroid, motion speed are detected and tracked. Through measuring the changes of these characteristics and judging the relations between the people and furniture nearby, the falls on furniture can be effectively detected. Experiment results demonstrated that our approach not only accurately and effectively detected falls on furniture, such as sofa and chairs but also distinguished them from other fall-like activities, such as sitting or lying down, while the existing methods have difficulties to handle these. In our experiments, our algorithm achieved 94.44% precision, 94.95% recall, and 95.50% accuracy. The proposed method can be potentially used and integrated as a medical assistance in health care and medical places and appliances.

Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics

The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices.

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A Two-Stream Approach to Fall Detection With MobileVGG

Behavior analysis through posture recognition is an essential research in robotic systems. Sitting with unhealthy sitting posture for a long time seriously harms human health and may even lead to lumbar disease, cervical disease and myopia. Automatic vision-based detection of unhealthy sitting posture, as an example of posture detection in robotic systems, has become a hot research topic. However, the existing methods only focus on extracting features of human themselves and lack understanding relevancies among objects in the scene, and henceforth fail to recognize some types of unhealthy sitting postures in complicated environments. To alleviate these problems, a scene recognition and semantic analysis approach to unhealthy sitting posture detection in screen-reading is proposed in this paper. The key skeletal points of human body are detected and tracked with a Microsoft Kinect sensor. Meanwhile, a deep learning method, Faster R-CNN, is used in the scene recognition of our method to accurately detect objects and extract relevant features. Then our method performs semantic analysis through Gaussian-Mixture behavioral clustering for scene understanding. The relevant features in the scene and the skeletal features extracted from human are fused into the semantic features to discriminate various types of sitting postures. Experimental results demonstrated that our method accurately and effectively detected various types of unhealthy sitting postures in screen-reading and avoided error detection in complicated environments. Compared with the existing methods, our proposed method detected more types of unhealthy sitting postures including those that the existing methods could not detect. Our method can be potentially applied and integrated as a medical assistance in robotic systems of health care and treatment.

https://www.mdpi.com/1424-8220/18/9/3119/pdf

A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading

Sitting posture has a close relationship with our health, keeping right sitting posture is important for people to avoid chronic diseases. However, automatic unhealthy sitting posture detection system is rare, especially for those based on computer vision technology. This paper proposes a new method of judging unhealthy sitting posture based on neck angle and torso angle detection using Kinect sensor. The method tracks neck angle and torso angle as two representative features from the depth image in a given period of time to judge whether the sitting posture is healthy or not. Experimental results show that the proposed method can judge sitting posture effectively for different unhealthy sitting types. Compared with the existing methods of action recognition, our method only needs a Kinect sensor without any other wearable sensors and is time efficient and robust because of only calculating two angles.

A New Kinect Approach to Judge Unhealthy Sitting Posture Based on Neck Angle and Torso Angle

The invention relates to a human body tumble detection method suitable for home indoor environments The method includes the following steps of: 1) scene analysis; 2) multi-feature extraction; and 3)behavior classification According to the step 1), a video capture device is adopted to capture video images Based on the Faster R-CNN algorithm, the step 1) further includes the following sub-stepsof 11) generating candidate frames; 12) directly convolving a full image based on the Fast R-CNN algorithm to obtain a feature image; and 13) mapping the positions of the candidate frames into thefeature image to obtain features corresponding each candidate frame According to the method of the invention, new feature and existing behavior feature-combined multi-feature fusion is adopted as anautomatic decision engine for tumble in a home indoor environment, and therefore, daily behaviors can be effectively classified; and the Fast R-CNN algorithm is applied to scene analysis so as to perform tumble detection in the home indoor environment; and tumble behaviors of some types and tumble behaviors of special types can be well distinguished from each other, and the accuracy of human bodytumble detection in the home indoor environment can be significantly improved

Hao Cui

Papers

Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics

A Two-Stream Approach to Fall Detection With MobileVGG

A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading

A New Kinect Approach to Judge Unhealthy Sitting Posture Based on Neck Angle and Torso Angle

Human body tumble detection method suitable for home indoor environments