# A Wireless Hand Grip Device for Motion and Force Analysis
## 1 Introduction  
Currently, new paradigms of natural interactions with computer are explored. One of the most used is hand gestures as control input in Human-Computer Interaction (HCI) [1, 2]. Hand movements are a means of non-verbal communication and can take the form of either simple actions or more complex ones. Therefore, it stands to reason that using the hands can be an intuitive method for communication with computers [1, 2]. New Virtual Reality [3] systems need, not only information about hand position, but also force values from the controlled input device. There is a need for kinematic and kinetic information in real time. Additionally, the combination of these above mentioned technologies are very useful in design applications for assistance and rehabilitation purposes, which is our main objective. Hands can be considered as a complex biomechanical structure with a high number of degrees of freedom (DOF) [4]. Therefore, it should be possible to use the hands as a high DOF control device in a wide range of applications (ludic or medical) [1, 2, 3, 4]. Two major types of technology for HCI can be identified, namely contact-based and vision-based devices. Contact-based devices (CBD) rely on physical interaction with the user. On the other hand, vision-based devices (VBD) analyze one or more video streams for determining hand motions. VBD technology can measure the motion of the end-effectors (fingers and the whole hand), but not the force done to catch an object. A large number of contact-based data gloves have been developed over the last thirty years [5], whereas vision-based tracking of the hands has been in development for about two decades [6]. We are aware of the complex challenges in hand gesture vision recognition and also of theirlimitations. Thus, we are more oriented to consider new low-cost contact-based systems, less invasive than the traditional gloves. Furthermore, the CBD can fill in the data gap that occurs with VBD during camera occlusions. An interesting subset of CBD is the wireless microelectromechanical based sensors (WMES). These systems use a set of interconnected sensors for motion and force measurements. Different aspects of fingers and hand motion are considered, and the combined data is used for higher-level feature extraction in gesture recognition and force performance [7]. To our knowledge, very few low-cost, wireless systems for hand tracking and force measurement are proposed in the literature. In [8], an interesting system to evaluate upper link task in Parkinson disease is proposed. This system considers a low-cost RGB-Depth camera using a monitor for visual feedback of the hand movements of the patient. The user's equipment is a pair of black silk gloves with imprinted color markers, which are used for the gestural control of the system and for task assessments. In the paper, a comparison between the offered solution and consumer devices is provided. In a precise qualitative evaluation, the system has been compared favorably with popular consumer alternatives. If we consider the system as a VBD, the forces done by the end user cannot be measured. A study using a data fusion approach is presented in [9]. The system combines the Nimble VR vision-based system, using the Kinect camera, with the contact based 5 DT Data Glove. This is an approach that uses the fusion of vision and contact, increasing the data completeness, and thus providing substantial advantage over the sole use of one device. In this system, the hands are detected using infrared depth information obtained from the Kinect camera. Using a previous hand's skeletal model, an estimation of the orientation and position of the hand and fingers is calculated. The 5 DT Data Glove was added to this setup, allowing for a double validation of the system. In the first method, a wooden articulated manikin hand model hand was used. The second method used dynamic hand movements with three human hands of different sizes to assess the robustness of the Kalman filter output.