## 3 Results and Discussion
On the one hand, they highlight the importance of thumb, index, and middle fingertips pressure sensors to distinguish free motion from manipulation. On the other hand, they reveal that the additional sensors s8 to s9 located in the palm also provide key information, except S10. Therefore, palm sensors should not be underestimated when designing data gloves with pressure sensors. In fact, palm is the part with the first  
Figure 6: One of the accessories for data glove calibration, according to a previously validated protocol [1]. The same accessory calibrating index MCP flexion and PIP flexion for CyberGlove (**left**) and VMG30 (**right**). and last contact with the objects when performing power grasps. This can be observed in sensor activation in tasks A1, A3, and A4 (all requiring power grasps), where additional palm sensors detected grasp or release. These results are consistent with previous studies analyzing hand pressure distribution in activities of daily living [15]. The only important difference regarding the results in the literature was the inactivity observed for pressure sensor s10. This palm spot was observed to present a higher activation of pressure sensors in [15] but this may be attributable to the pressure sensor size, as the used in previous studies was almost thrice the size of VMG30 sensors. The second spot observed to have higher activation in the aforementioned study was distal phalanx of index finger, which is consistent with the results here obtained using VMG30. The sensor that presented the most differences in contact detection was s2 (located at index fingertip), implying an anticipation of deactivation in release detection in tasks A5 and A6, both implying performing grasp types where the index finger was key (tripod pinch and intermediate, respectively). To better illustrate the results, Figure 8 presents a fusion of raw kinematic data, raw pressure sensors data, and visual analysis data while participant 1 was performing task A6, which was the one that presented the most differences between pressure sensors detection and visual analysis. It can be observed that s1 deactivation occurs long before the stabilization of the kinematic signal. Figure 7: Difference (in seconds) between automatic detection (activation/deactivation of pressure sensors (s1 to s10)) and visual analysis. Pressure sensors (s1–s10) labeled as described in Figure 1, tasks (A1–A6) labeled as detailed in Figure 4 and Table 3. Only difference values provided for first activation of pressure sensors in each task (to detect grasp) and last sensor deactivation (to detect release). Sensors activated in between are shaded in grey. Sensors not activated are not shaded (white cells). Differences lower than 0.5 s (absolute value) are shaded in green. Differences higher than 0.5 s (absolute value) are shaded in red. Moreover, it can be observed that the most differences in reaching mainly implied a delay in sensors detection (positive values), while in release they implied an anticipation in their deactivation (negative values). This led us to suspect that pressure sensors sensitivity is not enough to automatically differentiate product manipulation from release in ADLs. The contact detection clearly failed, and it can be attributable to different factors such as sensor sensitivity or bad fit of glove in small hands and the selected activities/products. In fact, participants reported some difficulties with the completion of some activities due the thickness and stiffness of the VMG30.