Phone-based gait analysis to detect alcohol usage

Kao, Hsin-Liu; Ho, Bo-Jhang; Lin, Allan C.; Chu, Hao-Hua

doi:10.1145/2370216.2370354

Cited by 39 publications

(18 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [24], the authors proposed a phonebased system to detect the gait anomalies of walking under the influence of alcohol. Based on accelerometer data from three participants, the authors extracted gait features to differentiate intoxicated walking patterns from regular patterns.…”

Section: Mobile Phones and Ubiquitous Healthcarementioning

confidence: 99%

DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones

Santani

Labhart

et al. 2018

IEEE Trans. on Mobile Comput.

122

View full text Add to dashboard Cite

Abstract-Alcohol consumption is the number one risk factor for morbidity and mortality among young people. In late adolescence and early adulthood, excessive drinking and intoxication are more common than in any other life period, increasing the risk of adverse physical and psychological health consequences. In this paper, we examine the feasibility of using smartphone sensor data and machine learning to automatically characterize and classify drinking behavior of young adults in an urban, ecologically valid nightlife setting. Our work has two contributions. First, we use previously unexplored data from a large-scale mobile crowdsensing study involving 241 young participants in two urban areas in a European country, which includes phone data (location. accelerometer, Wifi, Bluetooth, battery, screen, and app usage) along with self-reported, fine-grain data on individual alcoholic drinks consumed on Friday and Saturday nights over a three-month period. Second, we build a machine learning methodology to infer whether an individual consumed alcohol on a given weekend night, based on her/his smartphone data contributed between 8PM and 4AM. We found that accelerometer data is the most informative single cue, and that a combination of features results in an overall accuracy of 76.6%.

show abstract

Section: Mobile Phones and Ubiquitous Healthcarementioning

confidence: 99%

DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones

Santani

Labhart

et al. 2018

IEEE Trans. on Mobile Comput.

122

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

“…Smartphone-based alcohol consumption detection that evaluates a gait pattern captured by inertial sensors was proposed by [ 48 ], which labeled each gait signal with a Yes or a No in relation to alcohol intoxication. 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.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Also, Ref. [8] tried to detect drunk walking by using an accelerometer based on an assumption that the effects of alcohol intake on gait data are similar for each user. The studies that come closest to ours involve acceleration-based evaluation systems related to sport training and health care that evaluate skillfulness in sports and several health indicators.…”

Section: Related Workmentioning

confidence: 99%

Estimating Physical Characteristics with Body-worn Accelerometers Based on Activity Similarities

Masuda

Maekawa

2016

Journal of Information Processing

View full text Add to dashboard Cite

This paper describes our experimental investigation of the end user physical characteristics (e.g., gender, height, weight, dominant hand, and skill at sport) that can be successfully estimated solely from sensor data obtained during daily activities (e.g., walking and dish washing) from body-worn accelerometers. For this purpose we use the huge quantities of data that we have collected, which include 14,880 labeled activities obtained from 61 subjects. Our proposed method tries to estimate various kinds of characteristics based on our simple idea 'When the activity sensor data of two users are similar, the physical characteristics of the two users may also be similar.' We consider that estimating the end user's physical characteristics will enable us to realize new kinds of applications that automatically recommend information/services to an end user according to her estimated physical characteristics such as gender and weight.

show abstract

Phone-based gait analysis to detect alcohol usage

Cited by 39 publications

References 2 publications

DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones

DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones

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

Estimating Physical Characteristics with Body-worn Accelerometers Based on Activity Similarities

Contact Info

Product

Resources

About