###### Abstract  
There is a lack of research in using electromyography (EMG) signals to control a continuous passive motion (CPM) machine. This study aimed to develop an interface instrument for digitalising EMG signals and controlling a CPM machine. Methods: The proposed device was designed with the following: (1) a signal processing unit which converted the EMGs from analogue to digital for the controller; (2) a personal computer which stored and displayed the EMG signals; (3) an LCD device to display the running angle of the CPM; and (4) a microcontroller unit to control the input/output signals and process the algorithm, driving the CPM. To validate the reliability of the proposed system, a total of 600 EMG trials were collected from 10 healthy subjects by using the proposed device via the Delsys(r))))))))))))))))))))((((())))))))))))((())))))))))}})))))))))((()  
Signals produced from an individual's body are bio-signals and can potentially be used as the interface of human-computer devices [4]. Among the bioelectric signals, EMG is tested to be the basis of a natural means of HMI, i.e., an alternate input mechanism. An input device established using EMG is a natural means of HMI. The electronic activities initiated by an individual's muscle movements can be translated and converted into computer control instructions for rehabilitation equipment [5;6]. Such approaches are enormously valued by physically incapacitated individuals and can potentially be applied as an interface for human-computer devices. Several researchers and applications have exploited electromyography (EMG) signals to realise control systems. In the previous methods for controlling CPM machines, a study by [PERSON] and [PERSON] (2020) proposed a CPM with a hybrid proportional integral and derivative (PID) and iterative learning controller (ILC), which could learn the trajectory tracking feedback in the presence of different sizes from the subject's leg [7]. Another way to improve the controlling performance of a CPM machine is through using a pneumatic artificial muscle (PAM) actuator as an alternative to an electric actuator brushless direct current motor (BLDCM) by using a Hybrid Neuro-PID control algorithm using the neural network as a control algorithm [8]. [PERSON] et al. (2019) developed a mobile phone application using App Inventor to control the CPM device instead of the main remote controller of a CPM machine [9]. This allowed for wireless connection to the CPM device, permitting simplified control and monitoring. [PERSON] and [PERSON] (2009) developed a motorised CPM/CAM knee joint that could provide active mechanical resistance to oppose the motion of the user by using a force sensor under the patient's foot pedal [10]. Nevertheless, few previous studies have employed EMG signals to drive a CPM machine for formulating HMI. Previously, some studies used EMG to drive machines in rehabilitation. [PERSON] et al. (2008, 2009) reported that an EMG-driven assistant system is better than a passive motion system for stroke patients with wrist treatment [11;12]; [PERSON] et al. (2009) showed that an EMG-switching robot can be used for hand exercises [13]; [PERSON] et al. (2013) displayed a design that applied EMG signals in a CPM for ankle movement [14]; [PERSON] et al. (2018) introduced three types of assistant systems and EMG was used to verify the effect of the systems on walking, but not for driving movements [15]. However, there is a lack of studies which use EMG in the lower limbs to drive CPM directly. Although these methods for controlling CPM could be useful, these devices lack patient interaction, which is essential in improving rehabilitation outcomes. Therefore, this present study aimed to use EMG signals as the input signal to drive the CPM device, as it would produce a more interactive, informative, precise, and effective approach since the EMG directly reflects the intention of movement.