A method to align the coordinate system of accelerometers to the axes of a human body: The depitch algorithm

Gietzelt, Matthias; Schnabel, Stephan; Wolf, Klaus-Hendrik; Büsching, Felix; Song, Bianying; Rust, Stefan; Marschollek, Michael

doi:10.1016/j.cmpb.2011.10.014

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…Phone orientation alignment: To align the phone orientation to correct phone displacement as the user walks, we utilize the method proposed by Gietzelt et al [39]. Their basic approach is to estimate a rotation vector along a unit sphere, which repositions the smartphone to an initial starting position, which can be expressed as in equation 27.…”

Section: ) Innovative Extensions To Vanilla Bi-lstmmentioning

confidence: 99%

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Balakrishnan

Nie

et al. 2021

IEEE Access

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Driving is a dynamic activity, which requires quick reflexes and decision making in order to respond to sudden changes in traffic conditions. Alcohol consumption impairs motor and cognitive skills, and causes many driving-related accidents annually. Passive methods of proactively detecting drivers who are too drunk to drive in order to notify them and prevent accidents, have recently been proposed. The effects of alcohol on a drinker's gait (walk) is a reliable indicator of their intoxication level. In this paper, we investigate detecting drinkers' intoxication levels from their gait by using neural networks to analyze sensor data gathered from their smartphone. Using data gathered from a large controlled alcohol study, we perform regression analysis using a Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architectures to predict a person's Blood Alcohol Concentration (BAC) from their smartphone's accelerometer and gyroscope data. We innovatively proposed a comprehensive suite of preprocessing techniques and model-specific extensions to vanilla CNN and bi-LSTM models, which are well thought out and adapted specifically for BAC estimation. Our Bi-LSTM architecture achieves an RMSE of 0.0167 and the CNN architecture achieves an RMSE of 0.0168, outperforming state-of-the-art intoxication detection models using Bayesian Regularized Multilayer Perceptrons (MLP) (RMSE of 0.017) and the Random Forest (RF), with hand-crafted features. Moreover, our models learn features from raw sensor data, obviating the need for hand-crafted features, which is time consuming. Moreover, they achieve lower variance across folds and are hence more generalizable.

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Section: ) Innovative Extensions To Vanilla Bi-lstmmentioning

confidence: 99%

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Balakrishnan

Nie

et al. 2021

IEEE Access

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“…Because some gait parameters, specified in the following, refer to a certain axis of the human body, ▶ Figure 2 shows the definition of all axes. The alignment of the axes of the sensor node to the human body was done using a previously developed algorithm [28].…”

Section: Gait Parametersmentioning

confidence: 99%

Measurement of Accelerometry-based Gait Parameters in People with and without Dementia in the Field

Gietzelt¹,

Wolf²,

Kohlmann³

et al. 2013

Methods Inf Med

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“…This can be done using auto-correlation methods [28]. Due to the fact that it cannot be expected that the sensor is always aligned in a certain way, virtual alignment to the axes of the human body has to be done [29]. Afterwards, gait parameters such as velocity, kinetic energy or compensation movements can be computed from each gait period.…”

Section: Sensor Application Areas In Healthcarementioning

confidence: 99%

Wearable Sensors in Healthcare and Sensor-Enhanced Health Information Systems: All Our Tomorrows?

et al. 2012

Self Cite

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Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuropsychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs.

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A method to align the coordinate system of accelerometers to the axes of a human body: The depitch algorithm

Cited by 13 publications

References 15 publications

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Measurement of Accelerometry-based Gait Parameters in People with and without Dementia in the Field

Wearable Sensors in Healthcare and Sensor-Enhanced Health Information Systems: All Our Tomorrows?

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