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

Park, Eunjeong; Lee, Sunghoon I.; Nam, Hyo Suk; Garst, Jordan H.; Huang, Alex; Campion, Andrew; Arnell, Monica; Ghalehsariand, Nima; Park, Sangsoo; Chang, Hyuk Jae; Lu, Daniel C.; Sarrafzadeh, Majid

doi:10.3414/me15-02-0008

Cited by 21 publications

(3 citation statements)

References 55 publications

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“…Task-oriented, arm-hand training using sensor measurement was introduced in [33], and a machine learning method with pressure sensor–embedded smart shoes discriminated the alcohol-induced gait [34]. …”

Section: Discussionmentioning

confidence: 99%

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

2017

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BackgroundThe pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients.ObjectiveThe 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.MethodsWe 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.ResultsSignal 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%.ConclusionsSensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients.

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Section: Discussionmentioning

confidence: 99%

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

2017

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“…The study by Kao and colleagues [ 48 ] did not examine the quantity of drinks consumed, but focused its analyses solely on classifying a subject as intoxicated or not, thus limiting applicability across different ranges of BAC. Park et al [ 49 ] used a machine learning classifier to distinguish sober walking and alcohol-impaired walking by measuring gait features from a shoe-mounted accelerometer, which is impractical to use in the real world. Arnold et al [ 9 ] also used smartphone inertial sensors to determine the number of drinks (not BAC), an approach which could be prone to errors given that the association between number of drinks and BACs varies by sex and weight.…”

Section: Related Workmentioning

confidence: 99%

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

Gharani

Suffoletto

Chung

et al. 2017

Sensors

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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.

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“…Common temporal gait parameters such as walking velocity, stride length, foot angle, gait variability, cadence, and foot clearance can currently be obtained in real time by automatic calculation in the microprocessor of an IMS [ 4 , 5 ]. In contrast, temporal gait parameters concerning bilateral lower limbs (GPBLLs) play more important roles in healthcare or daily activity monitoring applications, such as evaluation of walking ability [ 4 ], metabolic evaluation [ 6 ], daily fatigue monitoring [ 7 ], and alcohol use monitoring [ 8 ]. These parameters include the double support time (DST), which is defined as the duration when both bilateral lower limbs touch the ground in one gait cycle (GC), and the symmetry index of stride time ( SIS tr ) and symmetry index of stance phase time ( SIS ta ) between the two limbs.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Fukushi

Wang

et al. 2022

Sensors

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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.

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

Cited by 21 publications

References 55 publications

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

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

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

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

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