## 4 Conclusions  
A data glove with pressure sensors would allow automatic differentiation between free motion and manipulation, avoiding human reaction-time errors. Nevertheless, owing to the results obtained in this experiment, currently available gloves such as the VMG30 would require design improvements to fit the requirements for kinematics characterization during product manipulation. Firstly, pressure sensors included in these types of data gloves should have higher sensitivity for more accurate automatic detection of manipulation. This conclusion can be drawn after observing that the sensors did not offer additional precision regarding the visual analysis technique (which also implies human error). Moreover, the pressure sensor distribution within the glove should consider the palm, as this part is the first and last to be in contact with objects when performing power grasps, such as cylindrical or oblique, as observed in pressure sensor activation. Another important weakness of the glove is the wiring on the dorsum of the hand, which should be modified in order to reduce glove stiffness and bulkiness. This conclusion is motivated by the concerns raised by participants during the experiments and the conviction that the bulkiness reduction would favor the placement of accessories during the initial calibration procedure if necessary. Nevertheless, it could be solved by developing a calibration protocol to control posture using an optical motion tracking system instead of wooden accessories. Apart from favoring glove calibration, less bulky wiring and reduced stiffness would contribute to more realistic hand motion. Bulkiness reduction could be  
Figure 8: Raw data of all VMG30 glove sensors while participant 1 was performing A6. Raw data of each gauge labelled accordingly to Table 2. Pressure sensors raw data labelled accordingly to Figure 1. Visual analysis time stamps are marked with an arrow. accomplished by using a lighter wiring technology, such as textile circuit boards. Another alternative would be to reduce the number of pressure sensors on the glove. Moreover, it was observed that sensor detection varied depending on hand size. It can be attributable to the small area covered by sensors (\(\mathcal{O}\) = 20 mm) combined with a bad fit of the glove, which contributes to locating pressure sensors in areas of the hand where they are not intended to be. The covered area of pressure sensors could be extended by using other sensor technologies, such as textile pressure sensors, that cover larger areas. This improvement should be accompanied by an improved glove fit. Correct sizing is a key factor determining data accuracy, as studied in previous studies in the literature [21], and the importance of manufacturing different glove sizes is evident. In general, improving glove fit and reducing glove stiffness would also have an effect on manipulation dexterity, as observed in previous studies [22]. A holistic analysis of glove usability, considering aspects such as overall flexibility, loss of manipulation dexterity, or glove weight, among others, needs to be addressed. With the current characteristics of each type of glove and taking all the outcomes into account, a glove without pressure sensors, such as the CyberGlove, is still the best choice for experiments requiring product manipulation. Nevertheless, in experiments requiring precision in phase distinction, the best choice may be sensor products in order to avoid human error in detection and sensor error associated with a bad glove fit. In the same way, CyberGlove could also be improved by lightening its wiring and offering several glove sizes, especially in their versions measuring 22 DoFs, which include sensors to record distal interphalangeal joint kinematics and do not fit small/medium hand sizes [21]. The main limitation of this study is the low sample size in Phase I and Phase II, as they were intended to be a technical validation prior to the main research experiments using data gloves.