scholarly journals High-Efficiency Multi-Sensor System for Chair Usage Detection

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7580
Author(s):  
Alessandro Baserga ◽  
Federico Grandi ◽  
Andrea Masciadri ◽  
Sara Comai ◽  
Fabio Salice
Keyword(s):  
Daily Living ◽  
High Efficiency ◽  
Pressure Sensors ◽  
Fall Detection ◽  
Wearable Devices ◽  
Capacitive Coupling ◽  
Daily Routines ◽  
Domestic Environments ◽  
Sustainable System ◽  
Contact Sensors

Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.

scholarly journals SisFall: A Fall and Movement Dataset

2016 ◽  
Author(s):  
Angela Sucerquia ◽  
Jose David Lopez ◽  
Jesus Francisco Vargas-Bonilla
Keyword(s):  
Feature Extraction ◽  
Daily Living ◽  
Fall Detection ◽  
Wearable Devices ◽  
Activity Analysis ◽  
Literature Analysis ◽  
Movement Detection ◽  
Individual Activity ◽  
Validation Tests ◽  
New Strategies

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, that prevent authors to evenly compare their new proposals. Here, we present a dataset of falls and activities of daily living (ADL) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADL and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADL and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96~\% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where algorithms could be focused on. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages to develop new strategies with this new dataset as benchmark.

scholarly journals Flexible PEBAX/graphene electromagnetic shielding composite films with a negative pressure effect of resistance for pressure sensors applications

RSC Advances ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 1535-1543 ◽  
Author(s):  
Biao Zhao ◽  
Xi Zhang ◽  
Jiushuai Deng ◽  
Chun Zhang ◽  
Yang Li ◽  
...  
Keyword(s):  
Negative Pressure ◽  
High Efficiency ◽  
Pressure Effect ◽  
Composite Films ◽  
Pressure Sensors ◽  
Electromagnetic Shielding ◽  
Good Sensitivity ◽  
Emi Shielding ◽  
Shielding Properties

Graphene/PEBAX composite films present high-efficiency EMI shielding properties and good sensitivity as well as sensing stability.

scholarly journals A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1831
Author(s):  
Armando Collado-Villaverde ◽  
Mario Cobos ◽  
Pablo Muñoz ◽  
David F. Barrero
Keyword(s):  
Machine Learning ◽  
Elderly Population ◽  
Real Data ◽  
Fall Detection ◽  
Wearable Devices ◽  
Accelerometer Data ◽  
Detection Systems ◽  
Detection Software ◽  
Health Complications ◽  
Public Datasets

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

scholarly journals Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1354 ◽  
Author(s):  
Gaojing Wang ◽  
Qingquan Li ◽  
Lei Wang ◽  
Yuanshi Zhang ◽  
Zheng Liu
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Research Problem ◽  
Wearable Devices ◽  
The Past ◽  
Machine Learning Methods ◽  
Open Research ◽  
Different Types

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

scholarly journals eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4565 ◽  
Author(s):  
Fabián Riquelme ◽  
Cristina Espinoza ◽  
Tomás Rodenas ◽  
Jean-Gabriel Minonzio ◽  
Carla Taramasco
Keyword(s):  
Older Adults ◽  
Young People ◽  
Elderly Care ◽  
Fall Detection ◽  
The Elderly ◽  
Research Area ◽  
Wearable Devices ◽  
Learning Approaches ◽  
Negative Consequences ◽  
Public Datasets

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

scholarly journals Online Fall Detection using Recurrent Neural Networks on Smart Wearable Devices

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Mirto Musci ◽  
Daniele De Martini ◽  
Nicola Blago ◽  
Tullio Facchinetti ◽  
Marco Piastra
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Fall Detection ◽  
Wearable Devices ◽  
Smart Wearable

Fall Detection and Daily Living Activity Recognition Logic Regression

2020 ◽  
Vol 17 (8) ◽  
pp. 3520-3525
Author(s):  
J. Refonaa ◽  
Bandaru Suhas ◽  
B. V. S. Bhaskar ◽  
S. L. JanyShabu ◽  
S. Dhamodaran ◽  
...  
Keyword(s):  
Daily Living ◽  
Detection System ◽  
Fall Detection ◽  
Learning System ◽  
Detection Accuracy ◽  
Logic Regression ◽  
Daily Living Activity ◽  
Average Value ◽  
Different Types ◽  
Angular Velocities

It is a must to bring the fall detection system in to use with the increasing number of elder people in the world, because the most of them tend live voluntarily and at risk of injuries. Falls are dangerous in a few cases and could even lead to deadly injuries. A very robust fall detection system must be built in order to counter this problem. Here, we establish fall detection and recognition of daily live behavior through machine learning system. In order to detect different types of activities, including the detection of falls and day to-day activities, We use 2 shared archives for the accelerating and lateral speed data during this development. Logistic regression is used to determine motions such as drop, walk, climb, sit, stand and lie bases on the accelerating data and data on angular velocities. More specifically, the triaxial acceleration average value is used to achieve fall detection accuracy.

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