Relationship between Walking Speed and Step Detection Accuracy Using Wrist and Hip-Worn Actigraph GT3X+ monitors.

In waist-worn pedestrian navigation systems, the periodic vertical acceleration peak signal at body centre of gravity is widely used for detecting steps. Due to vibration and waist shaking interference, accelerometer output signals contain false peaks and thus reduce step detection accuracy. This paper analyses the relationship between periodic acceleration at pedestrian centre of gravity and walking stance during walking. An adaptive dual-window step detection method is proposed based on this analysis. The peak signal is detected by a dual-window and the window length is adjusted according to the change in step frequency. The adaptive dual window approach is shown to successfully suppress the effects of vibration and waist shaking, thereby improving the step detection accuracy. The effectiveness of this method is demonstrated through step detection experiments and pedestrian navigation positioning experiments respectively. The step detection error rate was found to be less than 0·15% in repeated experiments consisting of 345 steps, while the longer (about 1·3 km) pedestrian navigation experiments demonstrated typical positioning error was around 0·67% of the distance travelled.

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Activity Tracking Using Ear-Level Accelerometers

Frontiers in Digital Health ◽

10.3389/fdgth.2021.724714 ◽

2021 ◽

Vol 3 ◽

Author(s):

Martin A. Skoglund ◽

Giovanni Balzi ◽

Emil Lindegaard Jensen ◽

Tanveer A. Bhuiyan ◽

Sergi Rotger-Griful

Keyword(s):

Hearing Aids ◽

Environmental Changes ◽

Classification Tree ◽

Consumer Electronics ◽

Sensor Technology ◽

Detection Accuracy ◽

Activity Classification ◽

Activity Tracking ◽

Step Detection ◽

Motion Activity

Introduction: By means of adding more sensor technology, modern hearing aids (HAs) strive to become better, more personalized, and self-adaptive devices that can handle environmental changes and cope with the day-to-day fitness of the users. The latest HA technology available in the market already combines sound analysis with motion activity classification based on accelerometers to adjust settings. While there is a lot of research in activity tracking using accelerometers in sports applications and consumer electronics, there is not yet much in hearing research.Objective: This study investigates the feasibility of activity tracking with ear-level accelerometers and how it compares to waist-mounted accelerometers, which is a more common measurement location.Method: The activity classification methods in this study are based on supervised learning. The experimental set up consisted of 21 subjects, equipped with two XSens MTw Awinda at ear-level and one at waist-level, performing nine different activities.Results: The highest accuracy on our experimental data as obtained with the combination of Bagging and Classification tree techniques. The total accuracy over all activities and users was 84% (ear-level), 90% (waist-level), and 91% (ear-level + waist-level). Most prominently, the classes, namely, standing, jogging, laying (on one side), laying (face-down), and walking all have an accuracy of above 90%. Furthermore, estimated ear-level step-detection accuracy was 95% in walking and 90% in jogging.Conclusion: It is demonstrated that several activities can be classified, using ear-level accelerometers, with an accuracy that is on par with waist-level. It is indicated that step-detection accuracy is comparable to a high-performance wrist device. These findings are encouraging for the development of activity applications in hearing healthcare.

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Vivid Imagers Are Better at Detecting Salient Changes

Journal of Individual Differences ◽

10.1027/1614-0001.27.4.218 ◽

2006 ◽

Vol 27 (4) ◽

pp. 218-228 ◽

Cited By ~ 13

Author(s):

Paul Rodway ◽

Karen Gillies ◽

Astrid Schepman

Keyword(s):

Individual Differences ◽

Change Detection ◽

Visual Imagery ◽

Detection Task ◽

Level Of Detail ◽

Detection Accuracy ◽

Change Detection Task ◽

Term Change ◽

High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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Right-Hemisphere Specialization for Contour Grouping

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000252 ◽

2014 ◽

Vol 61 (5) ◽

pp. 331-339 ◽

Cited By ~ 1

Author(s):

Gregor Volberg

Keyword(s):

Right Hemisphere ◽

Contour Detection ◽

Global Processing ◽

Detection Accuracy ◽

Frequency Spectra ◽

Visual Hemifield ◽

Spatial Frequencies ◽

A Chain ◽

The Right ◽

Hemisphere Specialization

Previous studies often revealed a right-hemisphere specialization for processing the global level of compound visual stimuli. Here we explore whether a similar specialization exists for the detection of intersected contours defined by a chain of local elements. Subjects were presented with arrays of randomly oriented Gabor patches that could contain a global path of collinearly arranged elements in the left or in the right visual hemifield. As expected, the detection accuracy was higher for contours presented to the left visual field/right hemisphere. This difference was absent in two control conditions where the smoothness of the contour was decreased. The results demonstrate that the contour detection, often considered to be driven by lateral coactivation in primary visual cortex, relies on higher-level visual representations that differ between the hemispheres. Furthermore, because contour and non-contour stimuli had the same spatial frequency spectra, the results challenge the view that the right-hemisphere advantage in global processing depends on a specialization for processing low spatial frequencies.

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Deception and dating: Knowledge of tactics may improve detection accuracy.

Journal of Social Evolutionary and Cultural Psychology ◽

10.1037/h0099335 ◽

2009 ◽

Vol 3 (1) ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Allyson Barnacz ◽

Franco Amati ◽

Christina Fenton ◽

Amanda Johnson ◽

Julian Paul Keenan

Keyword(s):

Detection Accuracy

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Kinetic alterations independent of walking speed in elderly fallers

Archives of Physical Medicine and Rehabilitation ◽

10.1053/apmr.2000.5581 ◽

2000 ◽

Vol 81 (6) ◽

pp. 730-735

Author(s):

D. Casey Kerrigan ◽

Laura W. Lee ◽

Tanya J. Nieto ◽

John D. Markman ◽

James J. Collins ◽

...

Keyword(s):

Walking Speed

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Detection of Malicious Spatial-Domain Steganography over Noisy Channels Using Convolutional Neural Networks

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.4.mwsf-076 ◽

2020 ◽

Vol 2020 (4) ◽

pp. 76-1-76-7

Author(s):

Swaroop Shankar Prasad ◽

Ofer Hadar ◽

Ilia Polian

Keyword(s):

State Of The Art ◽

Visual Quality ◽

Channel Noise ◽

Detection Accuracy ◽

Noisy Channel ◽

Noisy Channels ◽

Reliable Transmission ◽

Reliable Detection ◽

Natural Noise ◽

Will Force

Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.

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