A Multi-Modal Sensing Glove for Human Manual-Interaction Studies

We present an integrated sensing glove that combines two of the most visionary wearable sensing technologies to provide both hand posture sensing and tactile pressure sensing in a unique, lightweight, and stretchable device. Namely, hand posture reconstruction employs Knitted Piezoresistive Fabrics that allows us to measure bending. From only five of these sensors (one for each finger) the full hand pose of a 19 degrees of freedom (DOF) hand model is reconstructed leveraging optimal sensor placement and estimation techniques. To this end, we exploit a-priori information of synergistic coordination patterns in grasping tasks. Tactile sensing employs a piezoresistive fabric allowing us to measure normal forces in more than 50 taxels spread over the palmar surface of the glove. We describe both sensing technologies, report on the software integration of both modalities, and describe a preliminary evaluation experiment analyzing hand postures and force patterns during grasping. Results of the reconstruction are promising and encourage us to push further our approach with potential applications in neuroscience, virtual reality, robotics and tele-operation

Modeling Target-Distractor Discrimination for Haptic Search in a 3D Environment

Moringen A, Aswolinkiy W, Büscher G, Walck G, Haschke R, Ritter H. Modeling Target-Distractor Discrimination for Haptic Search in a 3D Environment. In: IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. 2018

Tactile-Glove In-Hand-Manipulation experiment data from Cube- & Rubber-Duck-Rotation

Büscher G. Tactile-Glove In-Hand-Manipulation experiment data from Cube- & Rubber-Duck-Rotation. Bielefeld University; 2020.# Tactile Glove In-Hand-Manipulation Dataset *Detailed pictorial overview in `Info-InHandOrientation.pdf`.* - This dataset aims to harness the power of machine learning to decode in-hand object rotations based on human demonstrations. Initially, our emphasis is on deepening the machine's understanding by classifying haptic cues on an object such as contacting, moving in specific directions, and guiding for each finger and the palm. Ultimately, the intention is to deduce also the current orientation of an object. The long-term goal is to apply this knowledge, enabling a robotic hand to replicate in-hand rotations of analogous objects — a prime example of skill transfer in action. ## CONTENTS: 1. Dataset Summary 2. Experiment Details 3. Equipment Specs 4. Data Playback ### Objectives: - Classify six rotation directions for cube and rubber-duck. - Predict 3D angle of rotation, object orientation, and haptic cues. - Forecast sensory cues and finger movements based on 3D rotation. --- ## Dataset Summary ``` | Modality | Specs | Rate | | ---------------------------- | ------------------------------------ | -------- | | Tactile (TactileGlove) | 60 sensors (of 64 total), 12 bit | 1000 Hz | | Posture (Joint_states) | 15 sensors, 12 bit | 1000 Hz | | IMU Orientation (BNO08x) | 9 dimensions | 100 Hz | | Camera Feed (usb_cam) | 640x480 pixels, RGB, 8 bit compressed| 30 Hz | ``` ``` | In-Hand rotations | Repetitions | Duration | | ------------------------- | ------------------- | --------- | | Cube (6 directions) | 50 reps/direction | ~10 sec | | Rubber-Duck (6 directions)| 30 reps/direction | ~10 sec | | Free Rotation | 6 reps each object | Varies | ``` Datasets saved as ROS bag files, organized by date/time. Rotations have separate subfolders. --- ## Experiment Details For each rotation: 1. Grasp top, roll hand to expose palm (~180°). 2. Rotate object in one pre-defined direction (~10 revs). 3. Return hand to start and place object down. - **Cube**: 50 reps/direction, ~10 sec each. Link: [Cube-Rotation](https://pub.uni-bielefeld.de/download/2966879/2967344.zip) - **Rubber-Duck**: 30 reps/direction, ~10 sec each. Link: [Rubber-Duck-Rotation](https://pub.uni-bielefeld.de/download/2966879/2967345.zip) - **Free Rotation**: 6 reps each for cube/rubber-duck, varied directions. Link: [Free-Rotation](https://pub.uni-bielefeld.de/download/2966879/2967345.zip) --- ## Equipment Specs ### Cube - Hand-sized, 3D-printed with rounded edges. - 51mm³, Weight: 137g, ABS, 24 ArUco markers (17.25mm each). ### Rubber-Duck - Hand-sized, flexible plastic. - 100mm length, 70mm width & height, Weight: 46g. ### Camera: Logitech HD webcam C615 - 30 Hz framerate. - Calibration details [here](http://wiki.ros.org/camera_calibration). ### µGlove - Captures hand movements/tactile contacts. - Tactile: 60 cells at 1000 Hz. - Posture: 15 sensors at 1000 Hz. - Orientation: 9 DoF IMU ~100 Hz. --- ## Data Playback For 3D visualization, install ROS and AGNI-Tools. Instructions [here](https://github.com/ubi-agni/TactileGlove). Help: [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected])

Optimization of a data-glove, especially integration of tactile sensors

Büscher G. Optimierung eines Datenhandschuhs, insbesondere Erweiterung um eine Taktilsensorik. Bielefeld: Universitätsbibliothek; 2011.Beschrieben wird die Entwicklung eines neuartigen elastischen Kraftsensors und seine vielfache Adaption zu einem leichten Taktil sensitivem Handschuh. Der Ursprung des Bedarfs dieses Handschuhs liegt in der Erforschung menschlich haptisch/kinästhetischer Interaktionen. Von der Aufgabenformulierung bis zur Fertigungsreife wird ein methodisches Vorgehen nach VDI-2212/2222 dargelegt. Darunter eine systematische Ideenfindung, die zu zahlreichen Prototypen wie auch einem innovativen Verfahren zum Einbringen von Leiterbahnen in elektrisch leitfähigen Stoff führte

Flexible and stretchable fabric-based tactile sensor

Büscher G, Kõiva R, Schürmann C, Haschke R, Ritter H. Flexible and stretchable fabric-based tactile sensor. Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012) Workshop on Advances in Tactile Sensing and Touch based Human-Robot Interaction, Algarve, Portugal.In this extended abstract we introduce a novel fabric-based, flexible, and stretchable tactile sensor, capable of seamlessly covering natural shapes. Our developed sensor can have an arbitrary perimeter, can cover freeform surfaces and remains operational on top of soft padding such as a gel cushion, which is a prerequisite for building human-like, soft artificial palm and finger sensors. We discuss the construction of the sensor and evaluate its performance. The sensor is very robust and can withstand normal forces multiple magnitudes higher than what could be achieved by a human without sustaining damage

Flexible and stretchable fabric-based tactile sensor

Büscher G, Kõiva R, Schürmann C, Haschke R, Ritter H. Flexible and stretchable fabric-based tactile sensor. Robotics and Autonomous Systems. 2015;63(SI Advances in Tactile Sensing and Touch-based Human-Robot Interaction):244-252.We introduce a novel, fabric-based, flexible, and stretchable tactile sensor, capable of seamlessly covering natural shapes. As humans and robots have smoothly curved body parts, which move with respect to each other, the practical usage of traditional rigid tactile sensor arrays is limited. Rather, a flexible tactile skin is required. Our design allows for several tactile cells to be embedded in a single sensor patch, can have an arbitrary perimeter and can cover free-form surfaces. We discuss the construction of the sensor and evaluate its performance. Our flexible tactile sensor remains operational on top of soft padding such as a gel cushion, enabling to build a human-like soft tactile skin. The sensor allows pressure measurements from subtle (100kPa), which easily covers the common range for everyday human manual interactions. Due to a layered construction, the sensor is very robust and can withstand normal forces multiple magnitudes higher than what could be achieved by a human without sustaining damage. As an exciting application for the sensor, we describe the construction of a wearable tactile dataglove with 54 tactile cells and embedded data acquisition electronics. We discuss the necessary implementation details to sustain long term sensor performance also in the presence of moisture

Augmenting curved robot surfaces with soft tactile skin

Büscher G, Meier M, Walck G, Haschke R, Ritter H. Augmenting curved robot surfaces with soft tactile skin. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE); 2015: 1514-1519

(Best Paper Award Nomination)

Büscher G, Kõiva R, Schürmann C, Haschke R, Ritter H. Tactile dataglove with fabric-based sensors. Presented at the Humanoids 2012, Osaka, Japan.This paper introduces a novel, fabric-based, flexible, and stretchable tactile sensor, capable of seamlessly covering natural shapes. Humans and most visually appealing robots have curved body parts which limit the practical usage of traditional rigid tactile sensors. Our design allows for several tactile cells to be embedded in a single sensor patch, can have an arbitrary perimeter and can cover freeform surfaces. We discuss the construction of the sensor and evaluate its performance. Our flexible tactile sensor remains operational on top of soft padding such as a gel cushion, making building human-like soft artificial palm and finger sensors possible. The sensor allows force measurements from subtle (30N), which easily covers the common range for everyday human manual interactions. Due to a layered construction, the sensor is very robust and can withstand normal forces multiple magnitudes higher than what could be achieved by a human without sustaining damage. As an exciting application for the sensor, in the paper we also describe the construction of a wearable tactile dataglove with 54 tactile cells

A Multi-Modal Sensing Glove for Human Manual-Interaction Studies

Bianchi M, Haschke R, Büscher G, Ciotti S, Carbonaro N, Tognetti A. A Multi-Modal Sensing Glove for Human Manual-Interaction Studies. Electronics. 2016;5(3): 42