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

Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN

Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.

A Review on Flexible Electrochemical Biosensors to Monitor Alcohol in Sweat

The continued focus on improving the quality of human life has encouraged the development of increasingly efficient, durable, and cost-effective products in healthcare. Over the last decade, there has been substantial development in the field of technical and interactive textiles that combine expertise in electronics, biology, chemistry, and physics. Most recently, the creation of textile biosensors capable of quantifying biometric data in biological fluids is being studied, to detect a specific disease or the physical condition of an individual. The ultimate goal is to provide access to medical diagnosis anytime and anywhere. Presently, alcohol is considered the most commonly used addictive substance worldwide, being one of the main causes of death in road accidents. Thus, it is important to think of solutions capable of minimizing this public health problem. Alcohol biosensors constitute an excellent tool to aid at improving road safety. Hence, this review explores concepts about alcohol biomarkers, the composition of human sweat and the correlation between alcohol and blood. Different components and requirements of a biosensor are reviewed, along with the electrochemical techniques to evaluate its performance, in addition to construction techniques of textile-based biosensors. Special attention is given to the determination of biomarkers that must be low cost and fast, so the use of biomimetic materials to recognize and detect the target analyte is turning into an attractive option to improve electrochemical behavior.

Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC). The results showed that, by averaging over five continuous effective strides, all time parameters achieved precisions of no more than 30 ms and agreement at the “excellent” level, and the symmetry indexes of the stride time and stance phase time achieved precisions of 1.0% and 3.0%, respectively, and agreement at the “good” level. These results suggest our method is effective and shows promise for wide use in many daily healthcare or activity monitoring applications for healthy subjects.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.

Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.

Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.

Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

Alcohol Use Disorder in the Age of Technology: A Review of Wearable Biosensors in Alcohol Use Disorder Treatment

Biosensors enable observation and understanding of latent physiological occurrences otherwise unknown or invasively detected. Wearable biosensors monitoring physiological constructs across a wide variety of mental and physical health conditions have become an important trend in innovative research methodologies. Within substance use research, explorations of biosensor technology commonly focus on identifying physiological indicators of intoxication to increase understanding of addiction etiology and to inform treatment recommendations. In this review, we examine the state of research in this area as it pertains to treatment of alcohol use disorders specifically highlighting the gaps in our current knowledge with recommendations for future research. Annually, alcohol use disorders affect approximately 15 million individuals. A primary focus of existing wearable technology-based research among people with alcohol use disorders is identifying alcohol intoxication. A large benefit of wearable biosensors for this purpose is they provide continuous readings in a passive manner compared with the gold standard measure of blood alcohol content (BAC) traditionally measured intermittently by breathalyzer or blood draw. There are two primary means of measuring intoxication with biosensors: gait and sweat. Gait changes have been measured via smart sensors placed on the wrist, in the shoe, and mobile device sensors in smart phones. Sweat measured by transdermal biosensors detects the presence of alcohol in the blood stream correlating to BAC. Transdermal biosensors have been designed in tattoos/skin patches, shirts, and most commonly, devices worn on the ankle or wrist. Transdermal devices were initially developed to help monitor court-ordered sobriety among offenders with alcohol use disorder. These devices now prove most useful in continuously tracking consumption throughout clinical trials for behavioral treatment modalities. More recent research has started exploring the uses for physical activity trackers and physiological arousal sensors to guide behavioral interventions for relapse prevention. While research has begun to demonstrate wearable devices' utility in reducing alcohol consumption among individuals aiming to cutdown on their drinking, monitoring sustained abstinence in studies exploring contingency management for alcohol use disorders, and facilitating engagement in activity-based treatment interventions, their full potential to further aid in understanding of, and treatment for, alcohol use disorders has yet to be explored.

Wearable Electrochemical Sensors for the Monitoring and Screening of Drugs

Wearable electrochemical sensors capable of noninvasive monitoring of chemical markers represent a rapidly emerging digital-health technology. Recent advances toward wearable continuous glucose monitoring (CGM) systems have ignited tremendous interest in expanding such sensor technology to other important fields. This article reviews for the first time wearable electrochemical sensors for monitoring therapeutic drugs and drugs of abuse. This rapidly emerging class of drug-sensing wearable devices addresses the growing demand for personalized medicine, toward improved therapeutic outcomes while minimizing the side effects of drugs and the related medical expenses. Continuous, noninvasive monitoring of therapeutic drugs within bodily fluids empowers clinicians and patients to correlate the pharmacokinetic properties with optimal outcomes by realizing patient-specific dose regulation and tracking dynamic changes in pharmacokinetics behavior while assuring the medication adherence of patients. Furthermore, wearable electrochemical drug monitoring devices can also serve as powerful screening tools in the hands of law enforcement agents to combat drug trafficking and support on-site forensic investigations. The review covers various wearable form factors developed for noninvasive monitoring of therapeutic drugs in different body fluids and toward on-site screening of drugs of abuse. The future prospects of such wearable drug monitoring devices are presented with the ultimate goals of introducing accurate real-time drug monitoring protocols and autonomous closed-loop platforms toward precise dose regulation and optimal therapeutic outcomes. Finally, current unmet challenges and existing gaps are discussed for motivating future technological innovations regarding personalized therapy. The current pace of developments and the tremendous market opportunities for such wearable drug monitoring platforms are expected to drive intense future research and commercialization efforts.

A Review of Smart Technologies Embedded in Shoes

Technological advancements in wearable devices have revolutionized smart shoes. Smart shoes are sometimes referred to as intelligent shoes or computer-based shoes. They are capable of recognizing and recording data from day-to-day activities by the user. Such smart shoes are designed with sensors, vibrating motors, GPS, wireless systems, and various other sensors/actuators for the comfort and benefit of the wearer. In the current manuscript, we are reviewing various technologies that are implemented in smart shoes.

An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of "just-in-time" injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption.

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients

Background

The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients.

Objective

The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing.

Methods

We extracted features of drift and pronation from accelerometer signals of wearable devices on the inner wrists of 16 stroke patients and 10 healthy controls. Signal processing and feature selection approach were applied to discriminate PDT features used to classify stroke patients. A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation.

Results

Signal processing by the PDT tool extracted a total of 12 PDT features from sensors. Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. Our proposed PDT classifiers had an area under the receiver operating characteristic curve (AUC) of .806 (SVM), .769 (RBFN), and .900 (RF) without feature selection, and feature selection improves the AUCs to .913 (SVM), .956 (RBFN), and .975 (RF), representing an average performance enhancement of 15.3%.

Conclusions

Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients.

Wearable Therapy - Detecting Information from Wearables and Mobiles that are Relevant to Clinical and Self-directed Therapy

BACKGROUND

This accompanying editorial provides a brief introduction into the focus theme "Wearable Therapy".

OBJECTIVES

The focus theme "Wearable Therapy" aims to present contributions which target wearable and mobile technologies to support clinical and self-directed therapy.

METHODS

A call for papers was announced to all participants of the "9th International Conference on Pervasive Computing Technologies for Healthcare" and was published in November 2015. A peer review process was conducted to select the papers for the focus theme.

RESULTS

Six papers were selected to be included in this focus theme. The paper topics cover a broad range including an approach to build a health informatics research program, a comprehensive literature review of self-quantification for health self-management, methods for affective state detection of informal care givers, social-aware handling of falls, smart shoes for supporting self-directed therapy of alcohol addicts, and reference information model for pervasive health systems.

CONCLUSIONS

More empirical evidence is needed that confirms sustainable effects of employing wearable and mobile technology for clinical and self-directed therapy. Inconsistencies between different conceptual approaches need to be revealed in order to enable more systematic investigations and comparisons.