http://yoksis.bilkent.edu.tr/pdf/files/10.1016-j.patcog.2010.04.019.pdf

Comparative study on classifying human activities with miniature inertial and magnetic sensors

http://sclab.yonsei.ac.kr/courses/10TPR/10TPR.files/Gesture%20spotting%20with%20body-worn%20inertial%20sensors%20to%20detect%20user%20activities.pdf

Gesture spotting with body-worn inertial sensors to detect user activities

We propose a two-stage recognition system for detecting arm gestures related to human meal intake. Information retrieved from such a system can be used for automatic dietary monitoring in the domain of behavioural medicine. We demonstrate that arm gestures can be clustered and detected using inertial sensors. To validate our method, experimental results including 384 gestures from two subjects are presented. Using isolated discrimination based on HMMs an accuracy of 94% can be achieved. When spotting the gestures in continuous movement data, an accuracy of up to 87% is reached.

/pdf/detection-of-eating-and-drinking-arm-gestures-using-inertial-48fnwv702h.pdf

Detection of eating and drinking arm gestures using inertial body-worn sensors

While much progress has been made in wearable computing in recent years, input techniques remain a key challenge. In this paper, we introduce uTrack, a technique to convert the thumb and fingers into a 3D input system using magnetic field (MF) sensing. A user wears a pair of magnetometers on the back of their fingers and a permanent magnet affixed to the back of the thumb. By moving the thumb across the fingers, we obtain a continuous input stream that can be used for 3D pointing. Specifically, our novel algorithm calculates the magnet's 3D position and tilt angle directly from the sensor readings. We evaluated uTrack as an input device, showing an average tracking accuracy of 4.84 mm in 3D space - sufficient for subtle interaction. We also demonstrate a real-time prototype and example applications allowing users to interact with the computer using 3D finger input.

uTrack: 3D input using two magnetic sensors

A method is disclosed for controlling at least one remotely operated unmanned object. The method may involve defining a plurality of body movements of an operator that correspond to a plurality of operating commands for the unmanned object. Body movements of the operator may be sensed to generate the operating commands. Wireless signals may be transmitted to the unmanned object that correspond to the operating commands that control operation of the unmanned object.

System and method for controlling swarm of remote unmanned vehicles through human gestures

We present a gesture recognition algorithm from Euler angles acquired using multiple orientation sensors. This algorithm is a part of a system for controlling unmanned aerial vehicles (UAVs) in the presence of manned aircrafts on an aircraft deck. After exploring multiple approaches to arm gesture recognition, we investigate a real-time arm gesture recognition system using the IS-300 Pro Precision Motion Tracker by InterSense. Our work consists of (1) analyzing several gesture recognition approaches leading to a selection of an active sensor, (2) gesture modeling using Euler angles, (3) low-level gesture characterization, and (4) model-based gesture classification algorithms. We have implemented and tested the proposed real-time arm gesture recognition system in a laboratory environment with a robot that represents an UAV surrogate.

/pdf/recognition-of-arm-gestures-using-multiple-orientation-4jcu4i8szz.pdf

Recognition of arm gestures using multiple orientation sensors: gesture classification

We present a solution to repeatability assessment of an arm gesture recognition system using multiple orientation sensors. We focus specifically on the problem of controlling unmanned aerial vehicles (UAVs) in the presence of manned aircrafts on an aircraft deck. Our goal is to design a robust UAV control with the same gesture signals as used by current flight directors for controlling manned vehicles. Given the fact that such a system has to operate 24 hours a day in a noisy and harsh environment, for example, on a Navy carrier deck, our approach to this problem is based on arm gesture recognition rather than on speech recognition. We have investigated real-time and system design issues for a particular choice of active sensors, such as, the orientation sensors of the IS-300 Pro Precision Motion Tracker manufactured by InterSense. Our work consists of (1) scrutinizing sensor data acquisition parameters and reported arm orientation measurements, (2) choosing the optimal attachment and placement of sensors, (3) measuring repeatability of movements using dynamic time warping (DTW) metric, and (4) testing performance of a template-based gesture classification algorithm and robot control mechanisms, where the robot represents an UAV surrogate in a laboratory environment.

J.-C. Lementec

Papers

Recognition of arm gestures using multiple orientation sensors: gesture classification

Recognition of arm gestures using multiple orientation sensors: repeatability assessment