# A Wireless Hand Grip Device for Motion and Force Analysis
## 1 Introduction
In papers [17; 18], the authors propose a cost-effective three-finger exoskeleton hand motion-capturing device [17] or a sensor glove with vibrotactile feedback and multiple finger joints and hand motion sensing [18]. The first device provides 12 DOFs data of finger motion by a unique level-gear structure as well as the use of six 3D magnetic sensors. The system enables feasibility of object manipulation as far as the needs go in various tasks in virtual environment. The second glove allows separate measurements of proximal and distal finger joint motions as well as position/orientation detection with an inertial measurement unit (IMU). These sensors and tactile feedback induced by coin vibration motors at the fingertips are integrated within a wireless, easy-to-use, and open-source system. The main advantages of this system are an easy-to-use and low-cost system with wireless connection to the host computer. From a holistic point of view, we can analyze the human motion with multisensorial information. This perspective is proposed in [19]. The author presents a generalized framework integrating multiple modules: sensor integration, signal preprocessing, correlation study of sensory information, and motion identification. Three types of sensors are integrated to simultaneously capture the finger angle trajectories, the hand contact forces, and the forearm electromyography (EMG) signals. The paper shows accurate results and effective solutions for motion capturing, data synchronization and segmentation, correlation study of the sensory information, and motion recognition. The proposed framework integrates the state-of-the-art sensor technology, mature machine learning methods, and signal processing algorithms. Along the same lines, Ref. [20] shows a complete review of multimodal human hand motion sensing and analysis. The work is current and includes the most recent data gloves available in the market. Additionally, clear figures and diagrams show all multimodal force-based and vision-based sensing information flow and signal processing. The work done in [21] offers a new review on multimodal human hand motion sensing. In this novel paper, a summary of hand motion analysis methods, including contact and non-contact devices, is presented. Additionally, surface electromyography (EMG) is analyzed as a tool for evaluation of the biofeedback of muscle movements by measuring the EMG signal on the surface of the skin. In Ref. [22], another data glove solution for hand motion tracking and gesture recognition is proposed. The KHU-I data glove consists in three tri-axis accelerometer sensors, one controller, and one Bluetooth module. The system can send full information to the PC through wireless communication using Bluetooth. The implemented 3-D digital hand model is based on the kinematic chain theory utilizing ellipsoids and joints. Due to the sensor's technical specification, we conclude that the system is highly intrusive. Furthermore, the number of hand gesture recognition possibilities are very limited (scissor, rock and paper). The system needs faster computation to reduce the time delay between actions recognition. The optoelectronic approach is an alternative for the evaluation of motor and cognitive capabilities. In Ref. [23], a method to capture motion of a pen through a mo-cap optoelectronic system was developed. Four IR passive markers were placed on a pen cap. Once a pen was equipped with the cap, track of tip was computed through a numeric algorithm using the 3D coordinates of markers provided by the optoelectronic system. The system is oriented to evaluate precisely the writing and drawing techniques on a tablet using this pen cap. The contribution is the new acquisition protocol using a new marker's configuration of the system. In this situation, the marker was not placed on the pen but on a surmounting cap, in order to allow a free and as natural as possible grasp of the pen.