Electromyographic Training Applications

Electromyography or skeletal muscle feedback is applied clinically to treat a range of physiological disorders from urinary incontinence through to chronic pain. Established as a clinical technique more than 20 years ago (Basmajian 1977) EMG biofeedback is also a popular method for training patients to regain muscle control lost due to accident or illness. Physiological rehabilitation can often be a slow process, particularly in the early stages of treatment, due to remaining muscle activity being so slight as to not cause any obvious physical movement. A seeming lack of ability to produce visible action within a muscle can compound the problem of slow progress by causing patients to lose motivation. However, these motivational issues can be overcome by visually amplifying and feeding back EMG activity. This visual amplification is often achieved through computer-based presentation techniques.

As described in the previous chapter, integrated biofeedback systems such as the ProComp/ BioGraph system provide facilities for feeding back EMG signals using computer animations and/ or sound. However, applications like BioGraph are currently limited in that these two forms of signal display are the only modes of representation available.

In 1997 Bowman reported on an Enhanced Sensory Feedback Device (ESFD) being used

clinically in the rehabilitation of patients with muscle control problems (Bowman 1997). The device detected EMG information from a subject, making the changing signal characteristics available to a computer. The role of the computer was to transform EMG activity into control commands to a simple interactive game. The game was a form of PacMan where progression of

the PacMan creature around a maze occurred only when the EMG signal amplitude was maintained above a predefined threshold value. Alternatively, the EMG signal drawn by the computer from the ESFD was used or to operate an electronic remote-control car.

The ESFD, developed by the Institute of Interventional Informatics (I3 _{2000), demonstrates a}

novel evolution of biofeedback interface technology. The technology behind the ESFD is an 8- channel electronic EMG sensing device called AnyWear (Lipson 1998). This device is a serially connected computer peripheral which streams EMG signal data to a purpose-built application called NeatTools (Salgado 1999). NeatTools pre-processes the signals in order to transform their changing amplitudes into simple control commands that can be used with its game-based training application. The NeatTools application also serves as the interface between the AnyWear sensing device and other electronic peripheral devices, such as the car.

The idea for the AnyWear system developed out of I3_{’s earlier experiences with another}

peripheral physiological sensing system, known as BioMuse. BioMuse was created by researchers at Stanford University who were interested in using EMG signals to create music in a hands-free manner (Knapp 1990). David Warner from I3 _{came across the 8-channel BioMuse}

device in 1991 and realised that by redesigning its interface, he could employ it as a computer input device for his quadriplegic patients. The first disabled user of the modified BioMuse system was an 18-month-old girl who used muscles around her eyes to move a smiling face icon around a computer display (Lusted 1996). A series of versions of the AnyWear system have subsequently enabled severely disabled subjects to interact with both computers and electronic objects within their environments through EMG signal control alone (Warner 1999).

The ability to apply a physiological signal to the control of an external device leads us to another common application of biofeedback-based technologies – as prosthetic devices for the disabled. Electromyographic limb control technology was first demonstrated in 1958 (as reported in Bennett 1981), but it has only been since the early 1980’s that medical engineers have been developing useable EMG-driven electronic prosthetic limbs (Saridis 1982, Kelly 1990). EMG prosthetics are most often upper-body limbs with embedded sensors that detect activity from muscles available around the site of the missing limb. Signals from these muscles are used to operate various aspects of the prosthetic’s functionality, for example bending of the elbow, flexion of the wrist and/or gripping actions can all be switched using the varying amplitude of individual EMG signals.

Learning to operate any form of EMG-driven prosthesis involves a period of training where a user experiments in order to discover which physical actions cause the desired control outcome. This period of feedback-based training can be carried out directly with the prosthetic or

alternately using a software training system such as the ProComp/ BioGraph. Existing research (Lake 1997) highlights the benefits of feedback training as part of the process of getting new amputees to accept and gain control of prosthetic limbs. Despite this, very few dedicated training technologies are available for prosthetic-specific EMG training. At the present time, recipients of prosthetic limbs learn to generate the right types of EMG response using the prosthetic itself as the feedback interface.

The next logical step beyond hands-free technologies for the disabled is to explore EMG as a mechanism for mainstream human-machine interaction. In 1998 Rosenberg (Rosenberg 1998) described the first interactive system designed specifically to demonstrate the potential of using EMG signals as a means of hands-free control for mobile computing applications. Rosenberg’s

Biofeedback Pointer consisted of three sets of electrodes positioned on the wrist in such a way as

to detect EMG activity related to motion in all three degrees of freedom of the wrist (rotation, forward/backward and side-to-side). EMG activity was collected using a custom built sensing device signals from which were fed to a software neural network running on a PC. The initial interface to Biofeedback Pointer was configured to act as a training environment for the neural network. This involved the system automatically moving a cursor around a screen so that a subject could follow the motion with his or her hand. Data collected from the wrist sensors could be fed to the neural network, which used the information to recognise patterns of signal activity from the three wrist muscles associated with wrist position. Subsequent to the network-training period, the system interface was changed so that it presented a biofeedback interface where the subject was given real-time visual feedback in the form of EMG signal-driven motion of a cursor on the screen.

EMG is the most well understood and immediately promising physiological signal source for hands-free human-machine interaction. This is due mostly to muscle control being a skill inherent in all able-bodied individuals. However, there are other signals used commonly in clinical biofeedback training that are now also being explored as potential hands-free control signal sources.