One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive PNS-Machine Interfaces was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PNS-Machine Interface (PMI) has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the peripheral nervous system (PNS) in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Abdominal aortic aneurysms (AAAs) require careful surveillance: rupture has a mortality rate of up to 90%.¹ Because the risk of rupture is well understood to be proportional to the AAA maximum diameter, the elucidation of risk factors for aneurysm enlargement is perhaps the most effective strategy to understand the natural history of AAA. It is incumbent not only to identify the factors broadly associated with AAA enlargement but also to clarify the nature of that enlargement, including analyses that are sensitive to the possibility that the expansion rate may not be constant but rather may increase over time.

We studied the relationship between grip force and external forearm pressure in nondisabled subjects using force myography (FMG). FMG uses a sensorized cuff surrounding the forearm to register the distributed mechanical force, detecting pressure on the sensors generated by the volumetric changes of the underlying musculo-tendinous complex. Each of nine nondisabled subjects donned the FMG cuff and applied grip forces to a cylindrical dynamometer; grip forces ranged from 0% to 100% of the subjects' maximum voluntary contraction. The cuff was positioned with seven force sensors located on both the anterior and posterior surfaces of the proximal forearm, but no attempt was made to match sensor placement with particular muscles or sites. Grip prediction was simply encoded as the rectified sum of the FMG sensor outputs. During grip and release cycles, the FMG waveforms of each subject correlated closely with his or her force waveforms (r > 0.89). FMG also correlated highly with the timing of grip onset and release (intraclass correlation coefficient (ICC(A,2)) = 0.99) and time to peak (ICC(A,2) = 0.91), with negligible lag. These results demonstrate that when applied to the forearm, FMG represents a grip force signature that may be useful for near-real-time proportional control of upper-limb prosthetic devices.

While surface electromyography (SEMG) can accurately register electrical activity of muscles during gait, there are no methods to estimate muscular force non-invasively. To better understand the mechanical behavior of muscle, we evaluated surface muscle pressure (SMP) in conjunction with SEMG. Changes in anterior thigh radial pressure during isometric contractions and gait were registered by pressure sensors on the limb. During isometric knee extensions by a single subject, SMP waveforms correlated well with SEMG (r=0.97), and SEMG onsets preceded those of SMP by 35-40 ms. SMP and SEMG signals were simultaneously recorded from the quadriceps of 10 healthy subjects during gait at speeds of 0.4, 0.8, 1.1, 1.4 and 2.2m/s. Muscle activity onset and cessation times were objectively determined for both modalities, and results showed high intra-class correlations. SMP waveforms were highly consistent from stride to stride, while SEMG waveforms varied widely. SEMG waveforms were typically brief, while SMP waveforms tended to be biphasic and outlasted the SEMG by approximately 40% of gait cycle at all speeds. These results are consistent with mechanical models of muscle, and demonstrate the use of SMP to estimate the timing of knee extensor muscle stiffness during gait.