## 5 Survey of Sensing Systems for Haptic Interface
A material for electronic skin applications that is self-healing and self-powered (**b5**) [93]. is analyzed to measure the intended letter using an adaptive pattern recognition algorithm. Although the device allows deafblind individuals to communicate, it is neither wearable nor portable. A new data glove relying upon inertial and magnetic measurement units (IMMUs) (namely, gyroscopes, accelerometers, and magnetometers) was introduced by [PERSON] et al. [110]. The projected data glove contains 18 inexpensive, wearable, and small-sized IMMUs. Cables link all sensor units to a centralized control device (Figure 14). The three-step ELM (Extreme Learning Machine)-based gesture recognition approach was also applied to the data generated by the designed data glove. Creating a gesture database and gathering motion data for various gestures constitute the initial stage. The training classifier is the second stage, followed by the experiment and analysis stages. To confirm the efficacy of the orientation estimation technique, three IMMU orientations were assessed in various contexts. According to performance assessments, the suggested data glove can precisely record 3D motions, and ELM-based algorithms can precisely identify the gestures. G-SCs are known for their remarkable performance in healthcare and rehabilitation applications. In recent years, advancements in the tools and techniques used to carry out these operations have made it feasible to treat and rehabilitate the upper limb in stroke patients. Additionally, based on various rehabilitation techniques and the severity of the patient's disability, several robots can be selected. Robotics employment does have the benefit of enabling a quantitative evaluation of patient enhancements. The upper limbs are currently rehabilitated using a variety of traditional methods. Examples include coercive, bilateral, mirror, and repeated task training. In this approach, the quality of life for those with difficulties after a stroke might be improved [116]. Careful follow-up and evaluation are necessary to measure hand motions that the patient can execute when customizing a rehabilitation program to help the patient restore the abilities that have been lost. Therefore, [PERSON] et al. designed an intelligent sensory glove customizable to the user's residual physiological functions [117]. The proposed system relies on the Arduino platform and comprises different bending and pressure sensors applied on three fingers and an LCD screen mounted on common gloves. The sensors track the amount of pressure the patient applies when first starting to grab the items. The device was tested on three healthy volunteers, who were asked to perform particular rehabilitation exercises to ascertain the joint angles' range. Furthermore, the force to carry out typical gripping tasks was measured using the integrated pressure sensors. Figure 14: The designed sensory glove with highlighted the main components (**left**) and worn by a user (**right**) [110]. A person's lifestyle is unavoidably altered by limb loss following an amputation. Loss of muscular and cognitive capabilities severely restricts their capacity to carry out everyday tasks and lowers their quality of life. An upper limb amputee can partially regain motor capabilities using a prosthesis based on an analysis of electromyography or powered by body movements. Nevertheless, the restoration of normal sensory abilities is still difficult. The B: Ionic glove, a prosthetic sensory system that may provide mechano-tactile stimuli on the user's arm as a function of the pressures generated at the glove's fingers, was introduced by [PERSON] et al. in [PERSON] et al. (2018) (Figure 15). Pressure pads were attached to the glove's fingers, which contain a conductive fluid. This fluid moves through a system of silicone tubes when pressure is applied, joining electrode pairs and constituting electrical circuits.