50,353 research outputs found
Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices
Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi
devices has gained attention with recent advances in wireless technology. HGR recognizes the human
activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing
them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction
and transformation to pre-process the raw CSI traces. However, these methods fail to capture
the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal
representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts
higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the
recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order
cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods
derived from information theory construct a robust and highly informative feature subset, fed as
input to the multilevel support vector machine (SVM) classifier in order to measure the performance.
The proposed methodology is validated using a public database SignFi, consisting of 276 gestures
with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home
environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of
97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average
recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was
96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio
3DTouch: A wearable 3D input device with an optical sensor and a 9-DOF inertial measurement unit
We present 3DTouch, a novel 3D wearable input device worn on the fingertip
for 3D manipulation tasks. 3DTouch is designed to fill the missing gap of a 3D
input device that is self-contained, mobile, and universally working across
various 3D platforms. This paper presents a low-cost solution to designing and
implementing such a device. Our approach relies on relative positioning
technique using an optical laser sensor and a 9-DOF inertial measurement unit.
3DTouch is self-contained, and designed to universally work on various 3D
platforms. The device employs touch input for the benefits of passive haptic
feedback, and movement stability. On the other hand, with touch interaction,
3DTouch is conceptually less fatiguing to use over many hours than 3D spatial
input devices. We propose a set of 3D interaction techniques including
selection, translation, and rotation using 3DTouch. An evaluation also
demonstrates the device's tracking accuracy of 1.10 mm and 2.33 degrees for
subtle touch interaction in 3D space. Modular solutions like 3DTouch opens up a
whole new design space for interaction techniques to further develop on.Comment: 8 pages, 7 figure
The passive operating mode of the linear optical gesture sensor
The study evaluates the influence of natural light conditions on the
effectiveness of the linear optical gesture sensor, working in the presence of
ambient light only (passive mode). The orientations of the device in reference
to the light source were modified in order to verify the sensitivity of the
sensor. A criterion for the differentiation between two states: "possible
gesture" and "no gesture" was proposed. Additionally, different light
conditions and possible features were investigated, relevant for the decision
of switching between the passive and active modes of the device. The criterion
was evaluated based on the specificity and sensitivity analysis of the binary
ambient light condition classifier. The elaborated classifier predicts ambient
light conditions with the accuracy of 85.15%. Understanding the light
conditions, the hand pose can be detected. The achieved accuracy of the hand
poses classifier trained on the data obtained in the passive mode in favorable
light conditions was 98.76%. It was also shown that the passive operating mode
of the linear gesture sensor reduces the total energy consumption by 93.34%,
resulting in 0.132 mA. It was concluded that optical linear sensor could be
efficiently used in various lighting conditions.Comment: 10 pages, 14 figure
Pre-Interaction Identification by Dynamic Grip Classification
We present a novel authentication method to identify users as they pick up a mobile device. We use a combination of back-of-device capacitive sensing and accelerometer measurements to perform classification, and obtain increased performance compared to previous accelerometer-only approaches. Our initial results suggest that users can be reliably identified during the pick-up movement before interaction commences
Interaction With Tilting Gestures In Ubiquitous Environments
In this paper, we introduce a tilting interface that controls direction based
applications in ubiquitous environments. A tilt interface is useful for
situations that require remote and quick interactions or that are executed in
public spaces. We explored the proposed tilting interface with different
application types and classified the tilting interaction techniques. Augmenting
objects with sensors can potentially address the problem of the lack of
intuitive and natural input devices in ubiquitous environments. We have
conducted an experiment to test the usability of the proposed tilting interface
to compare it with conventional input devices and hand gestures. The experiment
results showed greater improvement of the tilt gestures in comparison with hand
gestures in terms of speed, accuracy, and user satisfaction.Comment: 13 pages, 10 figure
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