**4. Test Platform Construction**

*4.2. Torque Estimation-Based Control Strategy*

**Figure 8.** Torque estimation-based control strategy.

module to provide appropriate assistance at the joint.

the motion control part based on torque closed−loop.

**4. Test Platform Construction** *4.1. Overview of the Upper Extremity Exosuit*

*4.1. Overview of the Upper Extremity Exosuit* We intend to take advantage of the abovementioned research to design a control logic for an upper extremity exosuit, so that it can perform rehabilitation training functions according to human intentions. As shown in Figure 7, this wearable system aims at providing active assistance for shoulder flexion/extension, shoulder adduction/abduction, and We intend to take advantage of the abovementioned research to design a control logic for an upper extremity exosuit, so that it can perform rehabilitation training functions according to human intentions. As shown in Figure 7, this wearable system aims at providing active assistance for shoulder flexion/extension, shoulder adduction/abduction, and elbow flexion/extension of the left arm. *Electronics* **2022**, *11*, x FOR PEER REVIEW 9 of 15 On the basis of the abovementioned hardware, the exosuit can be driven to assist the human limb coupled with a suitable control algorithm.

**Figure 7.** Pictures of an upper extremity exosuit: (**a**) the front; (**b**) the back. convenient for subsequent optimization work. **Figure 7.** Pictures of an upper extremity exosuit: (**a**) the front; (**b**) the back.

consists of two layers, namely, the intent analysis part based on torque estimation, and

When subjects wear this exosuit for collaborative movement, the upper controller will obtain the triaxial accelerations from the target muscle through the accelerometer embedded in the IMU, and synthesize them into an original MMG. After completing the EMD−based filtering operation, it screens out relatively pure signals and extracts the three characteristics including RMS, MPF, and SampEn. The trained RFR model uses these features as an input to estimate the expected joint torque value at the current moment. Fi-

The lower controller calculates the actual joint torque using the tension sensor readings at the end of the Bowden cable and compares it with the expected joint torque received from the upper layer to obtain their error value. Then, a standard PID algorithm generates motor drive commands based on the torque error, and controls the cable−driven

To realize this control strategy through a program code, we develop an embedded software based on the μC/OS III operating system. Five sub−tasks, including sensing data reception, signal processing, feature extraction, torque estimation, and motor servo control, are set up in order of priority from high to low. The execution frequency of each is assigned by setting different cycle times. Through the division of the abovementioned sub−task modules, we strengthen the real−time performance of programs under the premise of clarifying the control code logic for the upper extremity exosuit. In addition, it is

nally, the corresponding control commands will be sent to the lower layer.

It contains three sets of cable-driven modules, each of which is responsible for driving the bidirectional motion for one degree of freedom. The sensing network consists of three IMUs, six tension sensors, and three absolute encoders which are integrated in motors, and are in charge of completing multiple tasks, such as MMG signal collection, limb posture perception, human-machine interaction information acquisition, and servo motor state reading. As the main control board, STM32F407IGH6 will serve as the brain of the system to perform core functions such as feature extraction, motion intent identification, and motor servo control. Components communicate with each other through CAN bus for data feedback and instruction delivery. (**a**) (**b**)

On the basis of the abovementioned hardware, the exosuit can be driven to assist the

*Electronics* **2022**, *11*, x FOR PEER REVIEW 9 of 15

human limb coupled with a suitable control algorithm.

On the basis of the abovementioned hardware, the exosuit can be driven to assist the human limb coupled with a suitable control algorithm. **Figure 7.** Pictures of an upper extremity exosuit: (**a**) the front; (**b**) the back.

#### *4.2. Torque Estimation-Based Control Strategy 4.2. Torque Estimation-Based Control Strategy*

As shown in Figure 8, the control logic framework of the upper extremity exosuit consists of two layers, namely, the intent analysis part based on torque estimation, and the motion control part based on torque closed-loop. As shown in Figure 8, the control logic framework of the upper extremity exosuit consists of two layers, namely, the intent analysis part based on torque estimation, and the motion control part based on torque closed−loop.

**Figure 8.** Torque estimation-based control strategy. **Figure 8.** Torque estimation-based control strategy.

When subjects wear this exosuit for collaborative movement, the upper controller will obtain the triaxial accelerations from the target muscle through the accelerometer embedded in the IMU, and synthesize them into an original MMG. After completing the EMD−based filtering operation, it screens out relatively pure signals and extracts the three characteristics including RMS, MPF, and SampEn. The trained RFR model uses these fea-When subjects wear this exosuit for collaborative movement, the upper controller will obtain the triaxial accelerations from the target muscle through the accelerometer embedded in the IMU, and synthesize them into an original MMG. After completing the EMD-based filtering operation, it screens out relatively pure signals and extracts the three characteristics including RMS, MPF, and SampEn. The trained RFR model uses these features as an input to estimate the expected joint torque value at the current moment. Finally, the corresponding control commands will be sent to the lower layer.

tures as an input to estimate the expected joint torque value at the current moment. Finally, the corresponding control commands will be sent to the lower layer. The lower controller calculates the actual joint torque using the tension sensor readings at the end of the Bowden cable and compares it with the expected joint torque re-The lower controller calculates the actual joint torque using the tension sensor readings at the end of the Bowden cable and compares it with the expected joint torque received from the upper layer to obtain their error value. Then, a standard PID algorithm generates motor drive commands based on the torque error, and controls the cable-driven module to provide appropriate assistance at the joint.

ceived from the upper layer to obtain their error value. Then, a standard PID algorithm generates motor drive commands based on the torque error, and controls the cable−driven module to provide appropriate assistance at the joint. To realize this control strategy through a program code, we develop an embedded software based on the μC/OS III operating system. Five sub−tasks, including sensing data reception, signal processing, feature extraction, torque estimation, and motor servo control, are set up in order of priority from high to low. The execution frequency of each is To realize this control strategy through a program code, we develop an embedded software based on the µC/OS III operating system. Five sub-tasks, including sensing data reception, signal processing, feature extraction, torque estimation, and motor servo control, are set up in order of priority from high to low. The execution frequency of each is assigned by setting different cycle times. Through the division of the abovementioned subtask modules, we strengthen the real-time performance of programs under the premise of clarifying the control code logic for the upper extremity exosuit. In addition, it is convenient for subsequent optimization work.

assigned by setting different cycle times. Through the division of the abovementioned sub−task modules, we strengthen the real−time performance of programs under the premise of clarifying the control code logic for the upper extremity exosuit. In addition, it is

#### **5. Experiment on Exosuit 5. Experiment on Exosuit**

#### *5.1. Reliability Analysis Experiment for Torque Estimation 5.1. Reliability Analysis Experiment for Torque Estimation*

*Electronics* **2022**, *11*, x FOR PEER REVIEW 10 of 15

The parameters of the RFR model are determined by offline training on a PC. After transplantation to the control system of the exosuit, an evaluation experiment needs to be carried out to examine its actual application effect for different people. We recruited three male volunteers aged 22–27 to complete this experiment. Among them, two subjects (marked as Subject 1 and Subject 2) who have taken part in the training data set collection for torque estimation, are selected to join the experimental group, and one (marked as Subject 3), who did not participate in that process, is assigned to the control group. It is worth noting that volunteers should not have done any high-intensity exercise 24 hours before the tests, to avoid affecting the physiological state of the muscles. During the experiment, they are told to exert an external force that changes approximately in accordance with the sine law on the measurement platform. All subjects knew and agreed with relevant experimental procedures in advance. Research related to this article was approved by the Laboratory Academic Committee of the State Key Laboratory of Robotics and System, Harbin Institute of Technology. The parameters of the RFR model are determined by offline training on a PC. After transplantation to the control system of the exosuit, an evaluation experiment needs to be carried out to examine its actual application effect for different people. We recruited three male volunteers aged 22–27 to complete this experiment. Among them, two subjects (marked as Subject 1 and Subject 2) who have taken part in the training data set collection for torque estimation, are selected to join the experimental group, and one (marked as Subject 3), who did not participate in that process, is assigned to the control group. It is worth noting that volunteers should not have done any high−intensity exercise 24 hours before the tests, to avoid affecting the physiological state of the muscles. During the experiment, they are told to exert an external force that changes approximately in accordance with the sine law on the measurement platform. All subjects knew and agreed with relevant experimental procedures in advance. Research related to this article was approved by the Laboratory Academic Committee of the State Key Laboratory of Robotics and System, Harbin Institute of Technology.

The embedded system, mounted on an upper extremity exosuit, calculates the estimated torque in real time, and sends them to a PC after being processed. The sensor on the measurement platform obtains the force data at the end of the arm, which is converted into the actual torque value in the PC. As it only aims to evaluate the reliability of torque estimation, we have shielded the subtask of the motor servo control in the program, so as to avoid the influence of man-machine coupling. The embedded system, mounted on an upper extremity exosuit, calculates the estimated torque in real time, and sends them to a PC after being processed. The sensor on the measurement platform obtains the force data at the end of the arm, which is converted into the actual torque value in the PC. As it only aims to evaluate the reliability of torque estimation, we have shielded the subtask of the motor servo control in the program, so as to avoid the influence of man−machine coupling.

Figure 9 demonstrates the elbow joint torque estimation results of the upper extremity exosuit on three subjects. In the experimental group, it is obvious that the estimated torque looks very close to the actual value in terms of magnitude and variation trend. Under this condition, the model performance behaves in a relatively stable manner, and the identification result remains rather accurate; however, in the control group, the estimated torque cannot effectively follow the change of the actual value. Figure 9 demonstrates the elbow joint torque estimation results of the upper extremity exosuit on three subjects. In the experimental group, it is obvious that the estimated torque looks very close to the actual value in terms of magnitude and variation trend. Under this condition, the model performance behaves in a relatively stable manner, and the identification result remains rather accurate; however, in the control group, the estimated torque cannot effectively follow the change of the actual value.

**Figure 9.** Elbow joint torque estimation results: (**a**) experimental result of Subject 1; (**b**) experimental result of Subject 2; (**c**) experimental result of Subject 3. **Figure 9.** Elbow joint torque estimation results: (**a**) experimental result of Subject 1; (**b**) experimental result of Subject 2; (**c**) experimental result of Subject 3.

We use the *RMSE* and *R*<sup>2</sup> introduced above to quantitatively describe the identification effect for different subjects. It can be seen from Table 1 that the *RMSE* of the experimental group is lower than that of the control group, indicating that the error between the actual and estimated value is smaller for the joint torque of Subject 1 and Subject 2. Moreover, the *R*<sup>2</sup> in the experimental group comes up to 100%, which, when closely compared with the control group, means that the trained RFR model can perform better when utilizing the biological signals of Subject 1 and Subject 2. We use the *RMSE* and *R* 2 introduced above to quantitatively describe the identification effect for different subjects. It can be seen from Table 1 that the *RMSE* of the experimental group is lower than that of the control group, indicating that the error between the actual and estimated value is smaller for the joint torque of Subject 1 and Subject 2. Moreover, the *R* 2 in the experimental group comes up to 100%, which, when closely compared with the control group, means that the trained RFR model can perform better when utilizing the biological signals of Subject 1 and Subject 2.

**Table 1.** Evaluation results of torque estimation on three subjects.


**Table 1.** Evaluation results of torque estimation on three subjects.

*Electronics* **2022**, *11*, x FOR PEER REVIEW 11 of 15

From the above qualitative and quantitative description, the following two conclusions can be obtained. From the above qualitative and quantitative description, the following two conclusions can be obtained.


The reason may be that muscle activation varies among different people when they output the same joint torque, or different thicknesses of adipose layers more or less influences MMG propagation; therefore, when using the exosuit for power assistance, it is necessary to independently train a matching torque estimation model for the wearer based on his/her biological information. The reason may be that muscle activation varies among different people when they output the same joint torque, or different thicknesses of adipose layers more or less influences MMG propagation; therefore, when using the exosuit for power assistance, it is necessary to independently train a matching torque estimation model for the wearer based on his/her biological information.

#### *5.2. Efficiency Evaluation Experiment for Power Assistance 5.2. Efficiency Evaluation Experiment for Power Assistance*

In order to verify the actual power-assisted effect of this method, we selected a healthy subject, and collected his MMG signals at the brachioradialis, deltoid, and ectopectoralis to customize a set of RFR models for him. After transplanting these trained models to the embedded system, and enabling all the subtasks of control program, the subject wears the exosuit to perform elbow static flexion/extension, shoulder static flexion/extension, and shoulder static adduction/abduction on the measurement platform, and tries to complete three evaluation experiments. Other conditions and requirements are basically the same as the above experiment. An emergency stop switch needs to be held by the right hand all the way through the experiment, to ensure that the experiment can be stopped in time if an accident occurs. In order to verify the actual power−assisted effect of this method, we selected a healthy subject, and collected his MMG signals at the brachioradialis, deltoid, and ectopectoralis to customize a set of RFR models for him. After transplanting these trained models to the embedded system, and enabling all the subtasks of control program, the subject wears the exosuit to perform elbow static flexion/extension, shoulder static flexion/extension, and shoulder static adduction/abduction on the measurement platform, and tries to complete three evaluation experiments. Other conditions and requirements are basically the same as the above experiment. An emergency stop switch needs to be held by the right hand all the way through the experiment, to ensure that the experiment can be stopped in time if an accident occurs.

Figure 10 shows the performance evaluation experiments for joint movement assistance. We take three torque values, which are estimated by the RFR model, calculated by the tension sensor on the cable, and converted by the six-dimension force sensor on the measurement platform as human-exerted, exosuit-generated, and the total output, respectively. Figure 10 shows the performance evaluation experiments for joint movement assistance. We take three torque values, which are estimated by the RFR model, calculated by the tension sensor on the cable, and converted by the six−dimension force sensor on the measurement platform as human−exerted, exosuit−generated, and the total output, respectively.

**Figure 10.** Actual power−assisted experiments: (**a**) experiment for elbow static flexion/extension; (**b**) experiment for shoulder static flexion/extension; (**c**) experiment for shoulder static adduction/abduction. **Figure 10.** Actual power-assisted experiments: (**a**) experiment for elbow static flexion/extension; (**b**) experiment for shoulder static flexion/extension; (**c**) experiment for shoulder static adduction/abduction.

Figure 11 describes the changing situation of different torques in the typical time pe-

riod of each motion mode. Obviously, it can be seen that the upper extremity exosuit can produce additional assistance in the three degrees of freedom of the shoulder and elbow joints, although its actual output is smaller than the torque estimated by the physiological signal. This error can be attributed to the loss of power transmission caused by friction between cable and sheath, or the calculation model deviation induced by suit deformation. Moreover, the total output far exceeds the human effort, indicating that this ex-Figure 11 describes the changing situation of different torques in the typical time period of each motion mode. Obviously, it can be seen that the upper extremity exosuit can produce additional assistance in the three degrees of freedom of the shoulder and elbow joints, although its actual output is smaller than the torque estimated by the physiological signal. This error can be attributed to the loss of power transmission caused by friction between cable and sheath, or the calculation model deviation induced by suit deformation.

osuit can significantly enhance joint strength.

Moreover, the total output far exceeds the human effort, indicating that this exosuit can significantly enhance joint strength. *Electronics* **2022**, *11*, x FOR PEER REVIEW 12 of 15 *Electronics* **2022**, *11*, x FOR PEER REVIEW 12 of 15

**Figure 11.** Variations of different torques in each motion mode: (**a**) torque curves for elbow static flexion; (**b**) torque curves for shoulder static flexion; (**c**) torque curves for shoulder static abduction. **Figure 11.** Variations of different torques in each motion mode: (**a**) torque curves for elbow static flexion; (**b**) torque curves for shoulder static flexion; (**c**) torque curves for shoulder static abduction. (**a**) (**b**) (**c**) **Figure 11.** Variations of different torques in each motion mode: (**a**) torque curves for elbow static

We introduce the sum of absolute values (*ASUM*) to quantitatively reflect the average level of the three torques in different motion modes, and the corresponding results are shown in Figure 12. We introduce the sum of absolute values (*ASUM*) to quantitatively reflect the average level of the three torques in different motion modes, and the corresponding results are shown in Figure 12. flexion; (**b**) torque curves for shoulder static flexion; (**c**) torque curves for shoulder static abduction. We introduce the sum of absolute values (*ASUM*) to quantitatively reflect the average

$$ASIM = \frac{1}{n} \sum\_{i=0}^{n} |T\_i| \tag{13}$$

*n i =*0 **Figure 12.** *ASUM* of different torques under each joint movement. **Figure 12.** *ASUM* of different torques under each joint movement.

It is obvious that the sum of torque generated by the exosuit, and that exerted by a human, is not equal to the actual total output. The combined effect of factors such as identification error, transmission error, and calculation error, may lead to this gap between the expected and the actual. We expect to describe the power−assisted efficiency (*P*) through analyzing the ratio of *ASUMexosuit* to *ASUMtotal*. *ASUMexosuit ASUM* = 1 *n* ∑ |*T<sup>i</sup>* | *n i =*0 It is obvious that the sum of torque generated by the exosuit, and that exerted by a human, is not equal to the actual total output. The combined effect of factors such as iden-It is obvious that the sum of torque generated by the exosuit, and that exerted by a human, is not equal to the actual total output. The combined effect of factors such as identification error, transmission error, and calculation error, may lead to this gap between the expected and the actual. We expect to describe the power-assisted efficiency (*P*) through analyzing the ratio of *ASUMexosuit* to *ASUMtotal*.

1

∑ |*T<sup>i</sup>* |

*ASUM* =

$$P = \frac{ASUM\_{\text{crossulf}}}{ASUM\_{\text{total}}} \tag{14}$$

(13)

(13)

(14)

The calculation results show that when the upper extremity exosuit independently assists elbow static flexion/extension, shoulder static flexion/extension, and shoulder static adduction/abduction, the corresponding power−assisted efficiencies come up to 30.81%, 29.66%, and 25.78%,respectively. These data mean that when a person is equipped with this wearable robot, the output torque for each joint of the upper limb can be roughly reduced by a quarter to a third. analyzing the ratio of *ASUMexosuit* to *ASUMtotal*. *P* = *ASUMexosuit ASUMtotal* The calculation results show that when the upper extremity exosuit independently The calculation results show that when the upper extremity exosuit independently assists elbow static flexion/extension, shoulder static flexion/extension, and shoulder static adduction/abduction, the corresponding power-assisted efficiencies come up to 30.81%, 29.66%, and 25.78%, respectively. These data mean that when a person is equipped with this wearable robot, the output torque for each joint of the upper limb can be roughly reduced by a quarter to a third.

assists elbow static flexion/extension, shoulder static flexion/extension, and shoulder static adduction/abduction, the corresponding power−assisted efficiencies come up to 30.81%, 29.66%, and 25.78%,respectively. These data mean that when a person is equipped with this wearable

quarter to a third.

#### **6. Conclusions and Future Work**

In this article, we propose a MMG-based joint torque estimation algorithm which realizes the decoding from biological signals to limb strength, and transplant it into the control system of an exosuit to calculate the motor commands for assisting the multi-joint motion of the upper limb. Two sets of experiments are carried out to test the reliability of torque estimation and the efficiency of power assistance.

The data collection and signal processing methods used in this paper effectively establish the data sets which reflect human body information. The specially designed measurement platform can obtain the MMG of muscles and corresponding joint torque in a relatively accurate and convenient way. The HHT successfully eliminates the interference components in the original MMG signal, and lays a solid foundation for future extraction.

A technical approach to estimate joint torque from the MMG signal is built through training the RFR model offline. The reliability analysis experiment shows that this method can enable the exosuit to accurately identify the wearer's current joint torque, but the model parameters need to be specially trained for everyone.

A torque estimation-based control strategy is successfully applied to the motion control of the upper extremity exosuit. The efficiency evaluation experiment indicates that the exosuit using this algorithm can significantly enhance the limb strength of wearers.

Based on the actual execution of the research, we believe that the current work has the following limitations. First of all, the nonlinear disturbance caused by transmission friction and motion hysteresis significantly reduces the control performance and powerassisted efficiency of the upper extremity exosuit. In addition, the MMG-based torque estimation algorithm has limitations in its application. The model parameters may be trained separately for each person, and even each muscle.

Therefore, future work and research directions should aim to break through the above limitations. First, an error compensation algorithm for this cable-driven system should be introduced into the control logic, in an attempt to offset interferences caused by nonlinear characteristics. Second, more general intention recognition algorithms need to be studied further, which can meet the usage requirements of every wearer without additional training preparation.

**Author Contributions:** Methodology, data analysis, writing, Y.S.; conceptualization, project administration, W.D.; experimental verification, data preprocessing, W.L.; resources, supervision, L.H.; schematic analysis, resources, X.W.; structure design, resources, P.L.; formal analysis, software, Y.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the pre-research project in the manned spacefight field of China (Project Number 020202).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Acknowledgments:** The authors thank all those who have provided suggestions and assistance for this research and paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

