## 5 Testing and Evaluation
### Evaluation of the IoT Architectures
As the user also has access to the data via cloud, this data can be valuable feedback to users undergoing the therapy. The grasp, grip, and displace tests were carried out with three objects which included a ball, water bottle, and a spray paint bottle. The measurements of these objects are given in Table 10. We measured two parameters in these tests: the time taken to grasp an object after the command was issued, and the time taken to displace the object to a distance of 10 cm. Each of the users must grasp and grip the object first by issuing voice commands OPEN and CLOSE, and then displace the object 15 cm. The same tests were repeated with touch commands. For accurate results, the tests were carried out for 50 iterations, 25 times with voice command and 25 times with touch command. From Table 10, we can infer that the objects with smaller diameters take more time to grasp and grip when compared with the larger diameter objects. The time taken to displace an object was calculated, including the time to release an object from grip along with the displacement time. Figure 10: (**a**) The wearable device with a user’s hand, along with fixed and mobile gateways. (**b**) Live feedback from the smartphone while the wearable device was in operation. The control parameters and the current sensor graph are displayed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Desired** & & & & & \\
**Angle (deg)** & **Time—S1 (s)** & **Time—S2 (s)** & **Time—S3 (s)** & **Time—S4 (s)** & **Time—S5 (s)** \\ \hline
15 & 0.82 & 1.01 & 1.23 & 1.53 & 1.83 \\
30 & 1.11 & 1.37 & 1.80 & 2.42 & 3.01 \\
45 & 1.34 & 1.73 & 2.38 & 3.30 & 4.19 \\
60 & 1.56 & 2.09 & 2.96 & 4.18 & 5.37 \\
80 & 1.87 & 2.57 & 3.72 & 5.35 & 6.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Time taken by the HOD to reach the desired angle for different speeds. When users grip an object, the load current of the wearable system increases. This increase in current can be monitored and the grip force can be measured as the current can be related to the torque. This current, which is measured using a current sensor, is an indicator of whether the user is holding the object properly or not. The current measurement tests are carried out with the same three objects as in the previous test. The results are shown in the Figures 12-14. These figures show the variation in the current of digital servo motor in amperes with respect to the angle of the thumb part, when the users are trying to grasp and grip an object. The sudden overshoot in the graphs helps in determining the presence of an object at a particular angle. We can observe that the overshoot in Figure 14 (for paint bottle) occurs as early as 37\({}^{\circ}\) compared with the other two objects' (the ball and water bottle) current characteristics in Figures 12 and 13. As the paint bottle is heavier than the other two objects, the current overshoot starts earlier. Figures 12-14 were plotted by collecting the data for 200 iterations of each user picking each of the three objects. As the current measurement is independent of the control method, i.e., voice and touch, we only used the touch control method for this test. Compared with existing exoskeleton models [40; 41; 42], the weight of the proposed wearable is comparatively less. Our wearable device weighs about 270 g, which can be used for both hands when compared with other exoskeletons that can only be used for a single hand and weigh around 770 g [40], 430 g [41], and 731 g [42]. Like the previous tests, the data can be uploaded in the cloud for immediate feedback from the therapist. The data can be referenced at any time by both the therapist and the user for deciding the future course of action.