Ambient assistance service for fall and heart problem detection

Makhlouf, Amina; Boudouane, Isma; Saadia, Nadia; Ramdane-Chérif, Amar

doi:10.1007/s12652-018-0724-4

Cited by 31 publications

(19 citation statements)

References 57 publications

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“…Wearable devices are commonly used in sports, health applications, entertainment etc. [3] [30]. The availability of this category of sensors is inspiring more innovative research applications around activity recognition along with efficient processing techniques [38] [16].…”

Section: Activity Classificationmentioning

confidence: 99%

“Chatty Devices” and Edge-based Activity Classiﬁcation

Lakoju

Javed

Rana

et al. 2020

Preprint

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With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a “chatty” factory involves creating products which are able to send data back to the manufacturing/ production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may diﬀerent signiﬁcantly from actual usage. Understanding how this data can be used to support continuous product reﬁnement, and how the manufacturing process can be dynamically adapted based on the availability of this data provides a number of opportunities. We describe how data collected on product use be used to: (i) classify product use; (ii) associate a label with product use using unsupervised learning – making use of edge-based analytics; (iii) transmission of this data to a cloud environment where labels can be compared across diﬀerent products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy). Using a 6th gen. Apple iPad as a “chatty device” (with acceleration, orientation, angular velocity and magnetic ﬁeld sensors) we demonstrate how product use activities can achieve a classiﬁcation accuracy of 99.35%. A comparison is also undertaken with the Human Activity Recognition (HAR) data set, achieving an accuracy of 98%. Our approach demonstrates how semantic activity labels can be associated with product use, and subsequently used to improve product design.

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Section: Activity Classificationmentioning

confidence: 99%

“Chatty Devices” and Edge-based Activity Classiﬁcation

Lakoju

Javed

Rana

et al. 2020

Preprint

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“…A systematic review article by the World Health Organization (WHO) [ 5 ] in 2014 defined a fall incident as an inadvertent movement that comes to rest on the ground, floor, or other lower level, and does not include an intentional change in position. Likewise, the works in [ 6 , 7 ] have defined a fall incident as a sudden change in body position from either a standing or sitting posture to a lower position as a result of sudden slip or unstable body movements. Moreover, the WHO [ 8 ] has also reported that fall incidents are the second cause of death in the unintentional injury category, with an estimation of 646,000 cases each year.…”

Section: Introductionmentioning

confidence: 99%

Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System

Mohamed

Zulkifley

Ibrahim

et al. 2021

Sensors

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In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a person unintentionally drops down onto a lower surface. It is crucial to detect the occurrence of fall events as early as possible so that any severe fall consequences can be minimized. Nonetheless, a fall event is a sporadic incidence that occurs seldomly that is falsely detected due to a wide range of fall conditions and situations. Therefore, an automated fall frame detection system, which is referred to as the SmartConvFall is proposed to detect the exact fall frame in a video sequence. It is crucial to know the exact fall frame as it dictates the response time of the system to administer an early treatment to reduce the fall’s negative consequences and related injuries. Henceforth, searching for the optimal training configurations is imperative to ensure the main goal of the SmartConvFall is achieved. The proposed SmartConvFall consists of two parts, which are object tracking and instantaneous fall frame detection modules that rely on deep learning representations. The first stage will track the object of interest using a fully convolutional neural network (CNN) tracker. Various training configurations such as optimizer, learning rate, mini-batch size, number of training samples, and region of interest are individually evaluated to determine the best configuration to produce the best tracker model. Meanwhile, the second module goal is to determine the exact instantaneous fall frame by modeling the continuous object trajectories using the Long Short-Term Memory (LSTM) network. Similarly, the LSTM model will undergo various training configurations that cover different types of features selection and the number of stacked layers. The exact instantaneous fall frame is determined using an assumption that a large movement difference with respect to the ground level along the vertical axis can be observed if a fall incident happened. The proposed SmartConvFall is a novel technique as most of the existing methods still relying on detection rather than the tracking module. The SmartConvFall outperforms the state-of-the-art trackers, namely TCNN and MDNET-N trackers, with the highest expected average overlap, robustness, and reliability metrics of 0.1619, 0.6323, and 0.7958, respectively. The SmartConvFall also managed to produce the lowest number of tracking failures with only 43 occasions. Moreover, a three-stack LSTM delivers the lowest mean error with approximately one second delay time in locating the exact instantaneous fall frame. Therefore, the proposed SmartConvFall has demonstrated its potential and suitability to be implemented for a real-time application that could help to avoid any crucial fall consequences such as death and internal bleeding if the early treatment can be administered.

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“…Livak et al [30] combined the extracted features from the floor accelerometer and the microphone measurements to be considered in the classifier. Alternatively, the classification for various classes of movements could be adapted to different types of data [3,12,2,32]. To date, device-free activity recognition has attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

“…It is a fact that with the progress in technology, and the decrease of its cost, wearable devices are now commonly used in different areas, such as health applications, sports, entertainments, etc. [32,17,20]. Another motivating factor was the fact that research has witnessed a tremendous eagerness for applications on daily activity recognition, which resorts to wearable devices along with efficient processing techniques [2,36,18,45].…”

Section: Introductionmentioning

confidence: 99%

Fall detection and human activity classification using wearable sensors and compressed sensing

Kerdjidj

Ramzan

Ghanem

et al. 2019

J Ambient Intell Human Comput

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The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different Activities of Daily Living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8% of accuracy.

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Ambient assistance service for fall and heart problem detection

Cited by 31 publications

References 57 publications

“Chatty Devices” and Edge-based Activity Classiﬁcation

“Chatty Devices” and Edge-based Activity Classiﬁcation

Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System

Fall detection and human activity classification using wearable sensors and compressed sensing

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