Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes

2017 ◽  
Vol 56 (01) ◽  
pp. 74-82 ◽  
Author(s):  
Sunghoon I. Lee ◽  
Hyo Suk Nam ◽  
Jordan H. Garst ◽  
Alex Huang ◽  
Andrew Campion ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Alcohol Intoxication ◽  
Pressure Sensors ◽  
Well Being ◽  
Value Analysis ◽  
Single Feature ◽  
Ubiquitous Environment ◽  
Gait Features ◽  
Gait Monitoring

SummaryBackground: Alcohol ingestion influences sensory-motor function and the overall well-being of individuals. Detecting alcoholinduced impairments in gait in daily life necessitates a continuous and unobtrusive gait monitoring system.Objectives: This paper introduces the development and use of a non-intrusive monitoring system to detect changes in gait induced by alcohol intoxication.Methods: The proposed system employed a pair of sensorized smart shoes that are equipped with pressure sensors on the insole. Gait features were extracted and adjusted based on individual’s gait profile. The adjusted gait features were used to train a machine learning classifier to discriminate alcohol-impaired gait from normal walking. In experiment of pilot study, twenty participants completed walking trials on a 12 meter walkway to measure their sober walking and alcohol-impaired walking using smart shoes.Results: The proposed system can detect alcohol-impaired gait with an accuracy of 86.2% when pressure value analysis and person-dependent model for the classifier are applied, while statistical analysis revealed that no single feature was discriminative for the detection of gait impairment.Conclusions: Alcohol-induced gait disturbances can be detected with smart shoe technology for an automated monitoring in ubiquitous environment. We demonstrated that personal monitoring and machine learning-based prediction could be customized to detect individual variation rather than applying uniform boundary parameters of gait.

scholarly journals Development of Gait Monitoring System Based on 3-axis Accelerometer and Foot Pressure Sensors

2016 ◽  
Vol 10 (3) ◽  
pp. 199-206
Author(s):  
In-Hwan Ryu ◽  
Sunwoo Lee ◽  
Hyungi Jeong ◽  
Kihoon Byun ◽  
Jang-Woo Kwon
Keyword(s):  
Monitoring System ◽  
Pressure Sensors ◽  
Foot Pressure ◽  
Gait Monitoring

scholarly journals SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2374
Author(s):  
Paola Patricia Ariza-Colpas ◽  
Cristian Eduardo Ayala-Mantilla ◽  
Qaisar Shaheen ◽  
Marlon Alberto Piñeres-Melo ◽  
Diego Andrés Villate-Daza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Pressure Sensors ◽  
River Estuary ◽  
Machine Learning Techniques ◽  
Temperature Conductivity ◽  
Learning Techniques ◽  
Magdalena River ◽  
Prediction Techniques

This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.

Analytical Model of Micropyramidal Capacitive Pressure Sensors and Machine‐Learning‐Assisted Design

2021 ◽  
pp. 2100634
Author(s):  
Chao Ma ◽  
Gang Li ◽  
Longhui Qin ◽  
Weicheng Huang ◽  
Hongrui Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Analytical Model ◽  
Pressure Sensors

Classifying gait features for stance and swing using machine learning

2017 ◽  
Author(s):  
Chaitanya Nutakki ◽  
Jyothisree Narayanan ◽  
Aswathy Anitha Anchuthengil ◽  
Bipin Nair ◽  
Shyam Diwakar
Keyword(s):  
Machine Learning ◽  
Gait Features

scholarly journals Pervasive Lying Posture Tracking

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Learning Algorithms ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor System ◽  
Accurate Detection ◽  
Research Questions ◽  
Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

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