## 5 Survey of Sensing Systems for Haptic Interface
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Requirement** \\ \hline Monitoring hand movements & Up to 23 degrees of freedom (DoFs) are needed to effectively monitor hand gestures, including up to 4 DoFs for each finger (two for the first joint and one for each additional joint) and 3 DoFs for hand rotation. \\ \hline Tactile Response & Tactile feedback improves both the bidirectional interaction with the manipulated item and, thus, the user experience. \\ \hline Wearability & The device must be comfortable and simple to put on. \\ \hline Dimension & The device might be created in various sizes or made adaptable. \\ \hline Weight & The gloves should be light, often weighing between 50 and 300 g as they are placed on the hand. \\ \hline Power Source & The implementation of a low-power device is essential for this application. \\  & Energy-autonomous devices should be taken into account. \\ \hline Wireless communication & A wireless connection (such Bluetooth or Wi-Fi-based) is recommended for remote machine control. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic characteristics for intelligent gloves [93]. to quantify applied load to the hand or finger, which limits their applicability. By creating adaptable tactile sensors, actuators, and artificial e-skins, the issues mentioned above may be effectively addressed thanks to rapid improvements in flexible and printed electronics. For instance, glove-based HMIs might advance with the help of the e-skin-type solutions made possible by flexible electronics, as illustrated in Figure 13b, to offer a deeper intuitive user experience [103]. Smart gloves with built-in sensors have been used in gesture-based HMIs to assess finger position and identify important facial expressions [113]. Indeed, in G-SGs, the data provided by the embedded sensing devices are analyzed using various computing approaches to detect hand orientations [93]. The application of this technology for individuals with stroke, cerebral palsy, Parkinson, brain dysfunctions, and other conditions is now being investigated [113]. Orthopedic surgery has also been researched using virtual-reality-based rehabilitation, primarily for accident patients' hands or ankles [90; 114]. These systems were created primarily to address the shortcomings and errors of the manual methods used by practitioners to measure joint angles and progress in rehabilitation, respectively, using goniometers and questionnaires. Gesture-based smart gloves have also been used for AAC in addition to rehabilitation. For instance, due to their visual and hearing impairments, deafblind persons are supported by the fingerspelling communication method employing various finger motions to transmit letters, words, or sentences [115]. The output of sensors  
Figure 13: Manual user interaction without the use of any equipment (**a1**). The doctor assesses the finger angle with goniometers for therapeutic purposes (**a2**). A smart glove that recognizes gestures and uses a highly stretchy, polymer-enhanced strain sensor (**a3**) [111]. An interactive smart glove that uses touch and the Lorm alphabet to facilitate communication for the deafblind (**a4**) [112]. An IMMU-assisted smart glove for capturing and identifying 3D human gestures (**a5**) [110]. Gesture-based methods for deafblind remote communication utilizing PARLOMA, a revolutionary human–robot interaction system (**a6**). A soft intelligent glove for hand rehabilitation that combines touch and gesture methods (**a7**). For badminton, a sensory glove is employed to describe delicate hand motions (**a8**). Tactile feedback and integrated haptic sensing (**b1**). Gas-permeable, multipurpose electronic-skin electronic realized by laser-induced porous graphene and a spongy layer made of elastomer with a sugar template (**b2**). An energy-autonomous electronic skin (**b3**). A smart glove designed as an assistive device integrating touch sensors and actuators for persons who are deafblind (**b4**).