4,402 research outputs found
3D ranging and tracking using lensless smart sensors
Target tracking has a wide range of applications in Internet of Things (IoT), such as smart city sensors, indoor tracking, and gesture recognition. Several studies have been conducted in this area. Most of the published works either use vision sensors or inertial sensors for motion analysis and gesture recognition [1, 2]. Recent works use a combination of depth sensors and inertial sensors for 3D ranging and tracking [3, 4]. This often requires complex hardware and the use of complex embedded algorithms. Stereo cameras or Kinect depth sensors used for high precision ranging are instead expensive and not easy to use. The aim of this work is to track in 3D a hand fitted with a series of precisely positioned IR LEDs using a novel Lensless Smart Sensor (LSS) developed by Rambus, Inc. [5, 6]. In the adopted device, the lens used in conventional cameras is replaced by low-cost ultra-miniaturized diffraction optics attached directly to the image sensor array. The unique diffraction pattern enables more precise position tracking than possible with a lens by capturing more information about the scene
Detection of bimanual gestures everywhere: why it matters, what we need and what is missing
Bimanual gestures are of the utmost importance for the study of motor
coordination in humans and in everyday activities. A reliable detection of
bimanual gestures in unconstrained environments is fundamental for their
clinical study and to assess common activities of daily living. This paper
investigates techniques for a reliable, unconstrained detection and
classification of bimanual gestures. It assumes the availability of inertial
data originating from the two hands/arms, builds upon a previously developed
technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and
Gaussian Mixture Regression (GMR), and compares different modelling and
classification techniques, which are based on a number of assumptions inspired
by literature about how bimanual gestures are represented and modelled in the
brain. Experiments show results related to 5 everyday bimanual activities,
which have been selected on the basis of three main parameters: (not)
constraining the two hands by a physical tool, (not) requiring a specific
sequence of single-hand gestures, being recursive (or not). In the best
performing combination of modeling approach and classification technique, five
out of five activities are recognized up to an accuracy of 97%, a precision of
82% and a level of recall of 100%.Comment: Submitted to Robotics and Autonomous Systems (Elsevier
From Unimodal to Multimodal: improving the sEMG-Based Pattern Recognition via deep generative models
Multimodal hand gesture recognition (HGR) systems can achieve higher
recognition accuracy. However, acquiring multimodal gesture recognition data
typically requires users to wear additional sensors, thereby increasing
hardware costs. This paper proposes a novel generative approach to improve
Surface Electromyography (sEMG)-based HGR accuracy via virtual Inertial
Measurement Unit (IMU) signals. Specifically, we trained a deep generative
model based on the intrinsic correlation between forearm sEMG signals and
forearm IMU signals to generate virtual forearm IMU signals from the input
forearm sEMG signals at first. Subsequently, the sEMG signals and virtual IMU
signals were fed into a multimodal Convolutional Neural Network (CNN) model for
gesture recognition. To evaluate the performance of the proposed approach, we
conducted experiments on 6 databases, including 5 publicly available databases
and our collected database comprising 28 subjects performing 38 gestures,
containing both sEMG and IMU data. The results show that our proposed approach
outperforms the sEMG-based unimodal HGR method (with increases of
2.15%-13.10%). It demonstrates that incorporating virtual IMU signals,
generated by deep generative models, can significantly enhance the accuracy of
sEMG-based HGR. The proposed approach represents a successful attempt to
transition from unimodal HGR to multimodal HGR without additional sensor
hardware
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
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