Feedforward interfaces

In this chapter, the reader will be introduced to the state of the art control interfaces that are used in commercial as well as in research-based myoelectric prosthetic systems.

1.2.1.1 Commercial SoA

Currently, the most commonly used commercial myoelectric interfaces are designed as two-site electrode systems that implement direct proportional control. In these systems the two electrode systems are positioned over a pair of antagonist muscles (e.g., over the wrist flexor and extensor muscle groups in case of a transradial amputation), which are then used by the amputee to directly modulate the prosthesis’ movement velocity. The implementation of such system can be summarized as follows (Figure 1.5) [19]:

 The sEMG is pre-processed (i.e., amplified and filtered) in several stages via special electronics. Then, it is fed into an analog rectifier, or a similar circuit, that calculates its amplitude, either as mean-average value (MAV) or root-mean square (RMS) value or low-pass filtering;

 When the EMG amplitude is above a predefined threshold, the prosthesis moves with a velocity proportional to muscle activity - the higher the amplitude (i.e., the muscle activation), the faster the prosthesis moves. The movement direction is determined by the “larger-signal-wins” strategy: the channel with greater amplitude dictates the direction (e.g., hand open vs. close);

 Finally, in the case of a multi-DoF prosthesis, the control over each DoF is performed sequentially. The user needs to switch between, e.g., hand opening/closing and a wrist pronation/supination function via a pre-defined trigger (i.e., muscle pattern). To achieve this switching, quickly timed and strong muscle cocontractions or bursts are found to be particularly convenient;

Figure 1.5: SoA (commercial) direct control system for multi-DoF prosthesis. The user controls each DoF sequentially via two bipolar sEMG electrodes placed on the residual stump. The prosthesis will not react as long as the muscle activity is below the two predefined thresholds (T1, T2). Once the threshold is reached, the prosthesis starts the movement proportionally to the strength of the muscle contraction. The DoF switching is triggered by strongly co-contracting the two muscles for a short period of time.

This concept of myoelectric prosthesis control exists for well over 70 years [20] and has been the first one to be used in commercial myoelectric prostheses starting from the late 60’s [21]. Depending on the user needs and capabilities, the direct control systems are several: one-site control (“cookie crusher”), digital control, multi-level control, etc.

[19].

Direct control of DoFs is particularly effective following target muscle reinnervation (TMR) [22]. TMR is a surgical intervention that transfers the residual nerves from an amputated limb onto alternative muscle groups (e.g., plexus muscles) that are not biomechanically relevant since they are no longer attached to the missing arm. During this procedure, target muscles are denervated so that they can be reinnervated by the residual arm nerves which are surgically placed close to their target nerve or muscle.

After the post-op recovery process, the reinnervated muscles can serve as biological amplifiers of the amputated nerve motor commands (Figure 1.6). If the outcome was successful the amputee will be able to control directly, proportionally, and simultaneously up to 3-DoF’s in an intuitive manner because the sites of EMG detection

are well separated spatially.

Figure 1.6: Targeted muscle reinnervation procedure. Image adapted from [23].

1.2.1.2 Research SoA

Opposed to the commercially available, the academic SoA is virtually unconstrained in terms of its interface size and complexity. This is because, in the academic context, the practical implementation and device costs often play a secondary role in comparison to the possibly added functionality. Therefore, the feedforward MMIs developed in academic environment often use powerful machine learning algorithms. These algorithms are able to infer complex prosthesis movement by analyzing the muscle activation patterns in the residual limb. They have been in use since around 35 years [24] but they have gained popularity only with the development of powerful micro-processors in the early 90s. Since then they constitute the primarily used MMI in research. As for direct control, there is a great variety of machine learning interfaces.

They can be implemented in a virtually infinite number of ways: artificial-neural-networks/fuzzy logic [25], [26], [27] pattern-recognition [28], [29], [23] or regression [30], [31] based systems are just few of many existing examples in the literature. In most general sense, the pattern-recognition approach can be summed up as follows (Figure 1.7, [32]):

 The sEMG is acquired from several sites (e.g., usually from 6 to 12) distributed uniformly around the residual stump;

 Time and/or frequency features [25] are extracted over a sliding time window from each of the sEMG channel resulting in L x C feature matrix (where L represents the number of extracted features and C the number of channels). For performance reasons the time windows are usually 150 ms long with 50%

overlap;

 Thus extracted, the feature matrix is fed into a machine-learning algorithm.

Based on the prior knowledge, the machine-learning algorithm assigns the feature matrix into pre-designated movement classes (e.g., hand open, wrist supination, etc.) and moves the prosthesis accordingly. Interestingly, the proportionality of control is usually lost in this process, and additional cues need to be used in order for it to be inferred.

 It should be noted that the machine-learning algorithm must be trained before it is placed into use. The training process complexity will largely depend on the number of utilized classes, which is again correlated to the overall complexity of the prosthetic system;

Figure 1.7: SoA (research) sEMG pattern-recognition system for multi-DoF prosthesis control. Image adapted from [32].

Even though the machine learning algorithms have been in development for long time, their commercial implementation has been delayed until late 2014 with the introduction of the COAPT system [33]. The reasons behind this peculiarity will be discussed in detail in chapter 1.3.