1. Introduction

2. System Hardware Architecture

2.1. Master Controller

Due to the particularity of the oral environment, the master control chip placed in the oral cavity for monitoring the tongue muscle pressure needs to have the characteristics of stability, small size, light weight, low power consumption, and supporting for wireless communication to adapt to the particularity of the oral environment and provide an effective means to monitor tongue control devices. Consequently, the controller adopts the RSIC-V chip based on the Rocket kernel, and the characteristics of modularity make RISC-V have the characteristics of miniaturization and low energy consumption, which is crucial for embedded applications. The dynamic power of the Rocket kernel is 0.034mW/MHz, which is manufactured by TSMC 40GPLUS technology and its area is 0.14 mm2. In comparison, the Arm Cortex-A5 (32bit single-phase order) manufactured with the same process has a dynamic power of 0.08mW/MHz and an area of 0.27mm2. Running at the same frequency (>1GHz), the 64-bit Rocket scalar kernel consumes less power than the A5 kernel. To reduce power consumption, a power management unit is added to the SoC un the RSIC-V chip. If the SoC or a certain domain of the SoC is not used at certain times or in some applications, the power management unit will lower the voltage.

Figure 2. RSIC-V System Board.

Figure 2. RSIC-V System Board.

3. Experimental Methods

3.2. Control Algorithm

What is introduced above is mainly the information collection method adopted by the tongue control device. For a comprehensive control system, follow-up control methods should also be considered. In engineering practice, the most widely used regulator control law is PID control. When the structure and parameters of PID regulating controlled object cannot be fully mastered or there is no accurate mathematical model, the structure and parameters of system controller must be determined through experience and field debugging [15]. In practice, there are also PI and PD controls in PID control. PID controller is a systematic error, which is controlled by proportional, integral and differential calculation of the control quantity. When the pressure of the tongue muscle touching the pressure sensor changes, the movement of the wheelchair is also approximately uniform or uniformly accelerated and decelerated accordingly, allowing the user to feel more comfortable during the movement. The flow chart of the traditional PID algorithm is shown in Figure 8 below.

The specifications of the wheelchair DC motor are as follows: rated voltage U N = 2 4 V, rated current I N = 13 .7A, maximum speed nN = 75 0 r/min, allowable overload factor = 1.6, amplification factor of the thyristor triggered rectifier: Ks = 30, loop resistance R = 5.26 Ω , Electromagnetic time constant: Tl = 0.021s, PI type is selected for current regulator, and its transfer function is

(1)

ACR preemptive time constant: τ_i = Tl = 0.021s. The scale factor of ACR is

(2)

(3)

PI type is selected for current loop ASR regulator, and its transfer function is

(4)

Speed loop open loop gain:

(5)

(6)

(7)

Intelligent wheelchair is a multi-variable, nonlinear, time-varying system, which requires high accuracy and robustness, while it is difficult for traditional PID controllers to achieve this goal. Fuzzy PID control can effectively solve the problem of nonlinearity and rapid changing, and minimize the influence of disturbance and parameter change. It does not rely on the precise mathematical model of the controlled object; besides it can overcome the influence of nonlinear factors, and has a strong robustness to the parameter changes of the controlled object. It does not need to establish a mathematical model. According to the input and output result data of the actual system and referring to the operating experience of field operators, the system can controlled in real time [16]. Its fuzzy PID control flow chart is as shown in Figure 9 below, and its wheelchair rotation speed can be obtained by the encoder.

The adaptive fuzzy pid quantifies and fuzzifies the input quantity，which is the deviation E between the set output voltage and the feedback voltage of the motor and the deviation change rate Ec. Seven fuzzy control states are used to describe the speed deviation E in the motor speed regulation fuzzy controller system: Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Medium (PM), and Positive Big (PB). The corresponding universe of fuzzy sets after discretization is as follow: E*={-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6} . The fuzzy control rules of DC motors are summarized in Table 1,with a total of 49 control rules [17].

The fuzzy relation of the output control system is:

R_i=A_i×B_i×C_i

(8)

(9)

The parameters of PI controller are determined by engineering design method, and the output u i * of fuzzy PID controller obtained from the figure is:

u_i* = u_i + u_f

(10)

(11)

u_f = f(e,e_c)

(12)

3.3. Anti-misoperation Pressure Threshold Setting

We first averaged the simulated mis-touch tongue muscle pressure collected by 4 different experimenters using a carbon nanotube-PDMS pressure sensor (the simulated mis-touch tongue muscle pressure included the situation when the experimenters sit in a wheelchair and bump or they swallow saliva normally). For each experimenter, we measure three times for bumping on the wheelchair as well as normal swallowing of saliva respectively, and then collect the pressure under normal touch. Each experimenter normally touched the pressure sensor three times, and the experimental results are shown in Table 1. According to the data in the table below, we can conclude that when the pressure is greater than 0.12N, the pressure signal is the action signal.

Table 2. Normal Touch Pressure and Mis-touch Tongue Pressure Collection Form Currently.

Table 2. Normal Touch Pressure and Mis-touch Tongue Pressure Collection Form Currently.

Number of experiments	Pressure Data		Object
		A	B	C	D
1 2 3	Normal pressure Swallowing saliva pressure Bump pressure Normal pressure Swallowing saliva pressure Bump pressure Normal pressure Swallowing saliva pressure Bump pressure	0.124 0.042 0.022 0.122 0.05 0.024 0.128 0.037 0.028	0.138 0.048 0.025 0.132 0.063 0.027 0.139 0.042 0.027	0.131 0.041 0.029 0.135 0.061 0.022 0.134 0.036 0.024	0.127 0.043 0.021 0.129 0.057 0.035 0.132 0.034 0.022

4. System Testing and Evaluation

4.2. Center Click

The center click task evaluates tongue-controlled devices by speed and precision based on Fitts' law, which states the time to move a pointer to a target is related to the distance D between the current position of the pointer device and the target position and the size S of the target, as shown in Figure 1 1. The expression formula is:

$$t=a+\mathrm{b} \mathrm{l}\mathrm{o}\mathrm{g}2 (\mathrm{D}/\mathrm{S}+1)$$

(3)

where a , b represent empirical parameters which are based on the physical characteristics of the pointer device, the subject, and the environment [19].

Figure 1 1. Schematic Diagram of Fitts‘ Law.

Figure 1 1. Schematic Diagram of Fitts‘ Law.

According to the method described by Fanpeng Kong et al. [20], two circular targets of different diameters are given randomly as 30 and 60 pixels respectively. The target appears only one at a time with various distances from the center. Subjects manually manipulated the cursor to move from the center to the target and click on the center of the target as possible as one could. A total of 32 circular targets were randomly presented according to the subjects' clicks. In this paper, throughput and reaction time are used as reference indexes.

Figure 1 2. Center Click Task.

Figure 1 2. Center Click Task.

In this paper, throughput is used to represent the information transfer rate from the system to laptop computer, which is in bits/s. The average throughput is given by the following equation [15]:

$$TP=1/n{\sum }_{i=1}^n\left(\frac{1}{m}{\sum }_{j=1}^m\frac{{ID}_{ij}}{{MT}_{ij}}\right)$$

(14)

Among them, ID represents the difficulty index under the same conditions; MT represents the mean movement time; m represents the target number of subjects; n represents the total number of subjects, and the difficulty index under the same conditions is calculated according to the Shannon’s formula:

$$ID={log}_2(\frac{D}{W}+1)$$

(15)

wherein, D represents the distance from the pointer device, that is, the manual operation pointer, to the center of the circular target, and W represents the diameter of the circular target.

Response time refers to the time for the system to operate the cursor movement, represented by RT, and the unit is second.

The error rate refers to the proportion of subjects who fall outside the target range when they click on the target, expressed in ER.

Subjects performed 30 tongue touch tasks using iSTD, eSTD, and RSIC-V edge-based tongue control device, respectively, and the analysis results are shown in Figure 13 and Figure 14 below.

From the above results, it can be observed that the throughput of tongue-controlled devices based on RSIC-V edge computing is less than that of iSTD devices and eTDS devices because the raw data of tongue-controlled devices based on RSIC-V edge computing has been processed on the sensor side and specific instructions have been generated, which greatly reduces the requirements for data transmission rate and thus reduces the loss of CPU occupancy and energy in data transmission; and the reaction time of tongue-controlled devices based on RSIC edge computing in the central click task averages to be 0.16S, which is faster than that of iSTD devices (0.457S) and eTDS (0.227s) devices.

5. Conclusions

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