Analysis of Error-Agnostic Time- and Frequency-Domain Features Extracted From Measurements of 3-D Accelerometer Sensors

Dargie, Waltenegus; Denko, Mieso K.

doi:10.1109/jsyst.2009.2039735

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…We explored both timeand frequency-domain features. For a detailed description of the features, we refer the reader to [22] and [23].…”

Section: B Featuresmentioning

confidence: 99%

Human Activity Recognition Based on Wireless Electrocardiogram and Inertial Sensors

Farrokhi,

Dargie,

Poellabauer

2024

IEEE Sensors J.

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Wearable devices enable remote, long-term, and unobtrusive monitoring of patients in their everyday living and working environments. Remote health monitoring often involves monitoring physical and cardiac activities (exertions) in order to establish correlations between the two. With recent advances in sensor technologies and machine learning, the efficiency with which these activities can be recognized has been steadily improving. In this paper, we apply Convolutional Neural Networks (CNN) to measurements taken with wireless electrocardiograms and inertial sensors for Human Activity Recognition (HAR). Experimental results confirm that our approach is able to recognize a wide range of everyday activities with a high degree of accuracy. Specifically, activities such as Jumping, Running, and Sitting could be recognized with accuracy exceeding 99%, while activities such as Bending Over, Walking, Standing Up, and Climbing Stairs could be recognized with accuracy exceeding 90%. Overall, the results suggest that the combined use of inertial sensors and ECG leads to a better recognition accuracy. Likewise, the paper closely examines the contributions of individual sensors and if and to what extent their placement affects recognition accuracy.

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“…We explored both timeand frequency-domain features. For a detailed description of the features, we refer the reader to [22] and [23].…”

Section: B Featuresmentioning

confidence: 99%

Human Activity Recognition Based on Wireless Electrocardiogram and Inertial Sensors

Farrokhi,

Dargie,

Poellabauer

2024

IEEE Sensors J.

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“…For in depth comparison we refer to [38], [39] as well as related research from the field of activity recognition (cf. [40], [41], [42]).…”

Section: Shaking Mobile Devices Conjointlymentioning

confidence: 99%

ShakeUnlock: Securely Transfer Authentication States Between Mobile Devices

Findling

Muaaz

Hintze

et al. 2017

IEEE Trans. on Mobile Comput.

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As users start carrying multiple mobile devices, we propose a novel, token based mobile device unlocking approach. Mobile devices are conjointly shaken to transfer the authentication state from an unlocked token device to another device to unlock it. A common use case features a wrist watch as token device, which remains unlocked as long as it is strapped to the user's wrist, and a locked mobile phone, which is unlocked if both devices are shaken conjointly. Shaking can be done single-handedly, requires little user attention (users don't have to look at the device for unlocking it) and does not cause additional cognitive load on users. In case attackers gain control over the locked phone, forging shaking is difficult, which impedes malicious unlocks. We evaluate our approach using acceleration records from our 29 people sized ShakeUnlock database and discuss influence of its constituent parts on the system performance. We further present a performance study using an Android implementation and live data, which shows the true negative rate of observational attacks to be in the range of 0.8 -if an attacker manages to gain control over the locked device and shake it in parallel to the device owner shaking the token device.

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“…Dargie et al [7,8] provide quantitative and qualitative analysis of the robustness of time and frequency domain features to placement variations. The time domain features include: mean value, zero crossing rate, correlation coefficient, cross correlation and autocorrelation.…”

Section: Related Workmentioning

confidence: 99%

Placement variations and their diagnosis

Dargie

Schill

2010

Proceedings of the 4th ACM International Workshop on Context-Awareness for Self-Managing Systems

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This paper investigates the impact of placement and orientation variations on the quality of sensed data. Different types of human movements are considered, namely, balancing, skipping, leaping; climbing up and down a staircase, and running. For data collection, tri-axis accelerometer sensors are used. As target placements, arms, thighs, knees, ankle, and waist are considered. Likewise, four different orientation angles were considered during deployment, namely, 0, 30, 45, and 85 degrees. The features employed to investigate placement and orientation variations were zero\mean-value crossing rate, correlation coefficients, cross-correlation, and auto-correlation. A particular focus was given to steady slow movements (climbing up and down a staircase) and steady fast movements (running). Remarkably, the fast movements are less affected by placement variations in comparison to the slow movements. Moreover, it will be shown that the effect of orientation variations for all types of movements are insignificant when absolute acceleration instead of the accelerations of individual axes are independently considered.

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Analysis of Error-Agnostic Time- and Frequency-Domain Features Extracted From Measurements of 3-D Accelerometer Sensors

Cited by 9 publications

References 23 publications

Human Activity Recognition Based on Wireless Electrocardiogram and Inertial Sensors

Human Activity Recognition Based on Wireless Electrocardiogram and Inertial Sensors

ShakeUnlock: Securely Transfer Authentication States Between Mobile Devices

Placement variations and their diagnosis

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