Refine Search

New Search

Results: 18

(searched for: doi:10.1016/j.iot.2019.100124)
Save to Scifeed
Page of 1
Articles per Page
by
Show export options
  Select all
, , Minghui Qiu, Zhencan Peng,
ACM Transactions on Sensor Networks; https://doi.org/10.1145/3519302

Abstract:
The inevitable aging trend of the world’s population brings a lot of challenges to the health care for the elderly. For example, it is difficult to guarantee timely rescue for single-resided elders who fall at home. Under this circumstance, a reliable automatic fall detection machine is in great need for emergent rescue. However, the state-of-the-art fall detection systems are suffering from serious privacy concerns, having a high false alarm, or being cumbersome for users. In this paper, we propose a device-free fall detection system, namely G-Fall, based on floor vibration collected by geophone sensors. We first decompose the falling mode and characterize it with time-dependent floor vibration features. By leveraging Hidden Markov Model (HMM), our system is able to detect the fall event precisely and achieve user-independent detection. It requires no training from the elderly but only an HMM template learned in advance through a small number of training samples. To reduce the false alarm rate, we propose a novel reconfirmation mechanism using Energy-of-Arrival (EoA) positioning to assist in detecting the human fall. Extensive experiments have been conducted on 24 human subjects. On average, G-Fall achieves a 95.74% detection precision on the anti-static floor and 97.36% on the concrete floor. Furthermore, with the assistance of EoA, the false alarm rate is reduced to nearly 0%.
Sophini Subramaniam, Abu Ilius Faisal,
Published: 14 July 2022
Frontiers in Digital Health, Volume 4; https://doi.org/10.3389/fdgth.2022.921506

Abstract:
Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.
Abhishek Kumar, SwarnAvinash Kumar, Vishal Dutt, Ashutosh Kumar Dubey, Vicente García-Díaz
Published: 1 July 2022
Biomedical Signal Processing and Control, Volume 76; https://doi.org/10.1016/j.bspc.2022.103638

Abderrahim Zermane, Mohd Zahirasri Mohd Tohir, Mohd Rafee Baharudin, Hamdan Mohamed Yusoff
Published: 1 July 2022
Journal: Safety science
Safety science, Volume 151; https://doi.org/10.1016/j.ssci.2022.105724

Chunmiao Yuan, Pengju Zhang, Qingyong Yang, Jianming Wang
Published: 15 June 2022
Discrete Dynamics in Nature and Society, Volume 2022, pp 1-12; https://doi.org/10.1155/2022/8372291

Abstract:
For the problem of elderly people falling easily, it is very necessary to correctly detect the occurrence of falls and provide early warning, which can greatly reduce the injury caused by falls. Most of the existing fall detection algorithms require the monitored persons to carry wearable devices, which will bring inconvenience to their lives and few algorithms pay attention to the direction of the fall. Therefore, we propose a video-based fall detection and direction judgment method based on human posture estimation for the first time. We predict the joint point coordinates of each human body through the posture estimation network, and then use the SVM classifier to detect falls. Next, we will use the three-dimensional human posture information to judge the direction of the fall. Compared to the existing methods, our method has a great improvement in sensitivity, specificity, and accuracy which reaches 95.86, 99.5, and 97.52 on the Le2i fall dataset, respectively, whereas on the UR fall dataset, they are 95.45, 100, and 97.43, respectively.
Published: 23 March 2022
by MDPI
Journal: Applied Sciences
Applied Sciences, Volume 12; https://doi.org/10.3390/app12073276

Abstract:
Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly.
IEEE Access, Volume 10, pp 31306-31339; https://doi.org/10.1109/access.2022.3159235

Abstract:
This paper provides a comprehensive systematic literature review (SLR) of various technologies and protocols used for medical Internet of Things (IoT) with a thorough examination of current enabling technologies, use cases, applications, and challenges. Despite recent advances, medical IoT is still not considered a routine practice. Due to regulation, ethical, and technological challenges of biomedical hardware, the growth of medical IoT is inhibited. Medical IoT continues to advance in terms of biomedical hardware, and monitoring figures like vital signs, temperature, electrical signals, oxygen levels, cancer indicators, glucose levels, and other bodily levels. In the upcoming years, medical IoT is expected replace old healthcare systems. In comparison to other survey papers on this topic, our paper provides a thorough summary of the most relevant protocols and technologies specifically for medical IoT as well as the challenges. Our paper also contains several proposed frameworks and use cases of medical IoT in hospital settings as well as a comprehensive overview of previous architectures of IoT regarding the strengths and weaknesses.We hope to enable researchers of multiple disciplines, developers, and biomedical engineers to quickly become knowledgeable on how various technologies cooperate and how current frameworks can be modified for new use cases, thus inspiring more growth in medical IoT.
Published: 15 February 2022
by MDPI
Journal: Electronics
Abstract:
Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person’s death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people’s homes. The proposed non-wearable solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approaches are compared. The results showed that the combination of both models is possible and brings benefits to the system, achieving an accuracy of 92.5% without false negatives.
Published: 27 December 2021
by MDPI
Journal of Sensor and Actuator Networks, Volume 11; https://doi.org/10.3390/jsan11010002

Abstract:
Pervasive sensing with Body Sensor Networks (BSNs) is a promising technology for continuous health monitoring. Since the sensor nodes are resource-limited, on-node processing and advertisement of digested information via BLE beacon is a promising technique that can enable a node gateway to communicate with more sensor nodes and extend the sensor node’s lifetime before requiring recharging. This study proposes a Dynamic Light-weight Symmetric (DLS) encryption algorithm designed and developed to address the challenges in data protection and real-time secure data transmission via message advertisement. The algorithm uses a unique temporal encryption key to encrypt each transmitting packet with a simple function such as XOR. With small additional overhead on computational resources, DLS can significantly enhance security over existing baseline encryption algorithms. To evaluate its performance, the algorithm was utilized on beacon data encryption over advertising channels. The experiments demonstrated the use of the DLS encryption algorithm on top of various light-weight symmetric encryption algorithms (i.e., TEA, XTEA, PRESENT) and a MD5 hash function. The experimental results show that DLS can achieve acceptable results for avalanche effect, key sensitivity, and randomness in ciphertexts with a marginal increase in the resource usage. The proposed DLS encryption algorithm is suitable for implementation at the application layer, is light and energy efficient, reduces/removes the need for secret key exchange between sensor nodes and the server, is applicable to dynamic message size, and also protects against attacks such as known plaintext attack, brute-force attack, replaying attack, and differential attack.
Pooja, S. K. Pahuja,
Published: 27 October 2021
The publisher has not yet granted permission to display this abstract.
, Ali Ben Mrad, Brahim Hnich
Published: 1 October 2021
Procedia Computer Science, Volume 192, pp 1170-1179; https://doi.org/10.1016/j.procs.2021.08.120

The publisher has not yet granted permission to display this abstract.
, Mohd Hafiz Othman, Wan Zulkarnain Othman, Mohamad Fadhil Noor
Published: 17 April 2021
Lecture Notes in Electrical Engineering pp 269-279; https://doi.org/10.1007/978-981-33-6926-9_23

The publisher has not yet granted permission to display this abstract.
Published: 22 January 2021
by MDPI
Journal: Mathematics
Mathematics, Volume 9; https://doi.org/10.3390/math9030219

Abstract:
In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.
Ehsan Moghadas, , Reza Farahbakhsh
Published: 20 June 2020
Internet of Things, Volume 11; https://doi.org/10.1016/j.iot.2020.100251

The publisher has not yet granted permission to display this abstract.
Page of 1
Articles per Page
by
Show export options
  Select all
Back to Top Top