Wednesday 12:00-13:00
“A Preliminary Evaluation of a Body-Attached Multisensor Measurement Framework for Hand Gesture Recognition”
Rajarajan Ramalingame, Bilel Ben Atitallah and Olfa Kanoun
Abstract— This paper presents a framework for hand gesture recognition based on the information fusion from a body-attached multisensor technology comprising a smart glove, a smart band, and an inertial measurement unit (IMU). The smart glove is integrated with nanocomposite filament strain sensors developed from carbon nanotubes (CNT) dispersed in thermoplastic polyurethane (TPU) and extruded as filaments using a micro-compounder. The smart band is integrated with nanocomposite pressure sensors developed from CNTs dispersed in a silicone polymer, polydimethylsiloxane (PDMS), and deposited as a thin sheet that is cut as circular discs and coupled with underlying interdigital electrodes. The (IMU) comprising of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer and is fabricated along with the sensor interface and single processing unit. The paper elaborates on the development of the nanocomposite sensors and the performance of these three sensor technologies by studying ten American Sign Language (ASL) gestures representing numbers 1 to 10 without the involvement of a sophisticated machine learning algorithm for gesture classification. The outcome of this investigation provides valuable insights into the performance of the individual sensor technology in comparison to their counterparts and sets guidelines for further development of such body-attached systems in terms of the type and selection of sensor technology and the required number of sensors. The hardware architecture of the developed framework can be directly implemented as a potential tool for human-machine interface-related activities.
Keywords: Gesture recognition, Filament strain sensors, Nanocomposite pressure sensors, IMU, Body attached sensor network
“Design and Preliminary Testing of a Shoulder Exoskeleton based on a Soft Bellow Actuator”
Stanislao Grazioso, Teodorico Caporaso, Benedetta M. V. Ostuni and Antonio Lanzotti
Abstract— This paper describes the design and preliminary testing of a soft exoskeleton for the shoulder which supports the adbuction/adduction movements. The soft exoskeleton is based on a single soft bellow actuator, a pneumatically actuated system which is composed by multiple consecutive chambers that expands when compressed air is inflated till providing a desired motion. In this work we present the design, analysis, prototyping and testing of the soft bellow actuator as well as its preliminary integration into a first version of bellow-based soft exoskeleton for the shoulder.
Keywords: Soft Exoskeletons, Soft Actuators, Soft Robotics
“Understanding the Capabilities of FMG and EMG Sensors in Recognizing Basic Gesture Components“
Giuseppe Sanseverino, Dominik Krumm, Rajarajan Ramalingame, Chintan Malani, Rim Barioul, Olfa Kanoun and Stephan Odenwald
Abstract—Gestures are one of the most intuitive ways that humans use to interact with others or convey information. The idea that hand gestures could facilitate human-machine interaction has recently gained increasing interest among researchers. Various technologies have been investigated, providing both visual and sensor-based gesture recognition. While camera-based solutions suffer from the constraint of specific and expensive laboratories, wearable sensor-based solutions allow lower costs and higher flexibility, enabling gesture recognition even in public spaces. Although several solutions are available in the literature, most of them focus on specific sensor principles and specific gestures. The aim of this work is to recognize basic gesture components, defined as primary elements that compose more complex gestures, using both force myography (FMG) and electromyography (EMG), and to highlight their strengths and weaknesses. This will provide the foundation for the recognition of more complex human upper limb movements. To this end, a laboratory study was conducted with ten participants. FMG signals were collected by means of a wearable sensor network consisting of an instrumented smart band with eight pressure sensors and a wireless datalogger. EMG data were acquired using three commercial sensors. The recorded data were analyzed using k-nearest neighbor classifier and extreme learning machine algorithms. The results showed that the data recorded using FMG had higher accuracy in recognizing the ten different static hand gestures studied compared to the EMG data.
Keywords: Human-Machine Interaction, Gesture recognition, Body-Attached Sensor Networks, FMG, EMG