Quantification of functional hand grip using electromyography and inertial sensor-derived accelerations: clinical implications

Decoding of unimanual and bimanual reach-and-grasp actions from EMG and IMU signals in persons with cervical spinal cord injury

Objective. Chronic motor impairments of arms and hands as the consequence of a cervical spinal cord injury (SCI) have a tremendous impact on activities of daily life. A considerable number of people however retain minimal voluntary motor control in the paralyzed parts of the upper limbs that are measurable by electromyography (EMG) and inertial measurement units (IMUs). An integration into human-machine interfaces (HMIs) holds promise for reliable grasp intent detection and intuitive assistive device control.Approach. We used a multimodal HMI incorporating EMG and IMU data to decode reach-and-grasp movements of groups of persons with cervical SCI (n = 4) and without (control, n = 13). A post-hoc evaluation of control group data aimed to identify optimal parameters for online, co-adaptive closed-loop HMI sessions with persons with cervical SCI. We compared the performance of real-time, Random Forest-based movement versus rest (2 classes) and grasp type predictors (3 classes) with respect to their co-adaptation and evaluated the underlying feature importance maps.Main results. Our multimodal approach enabled grasp decoding significantly better than EMG or IMU data alone (p<0.05). We found the 0.25 s directly prior to the first touch of an object to hold the most discriminative information. Our HMIs correctly predicted 79.3 ± STD 7.4 (102.7 ± STD 2.3 control group) out of 105 trials with grand average movement vs. rest prediction accuracies above 99.64% (100% sensitivity) and grasp prediction accuracies of 75.39 ± STD 13.77% (97.66 ± STD 5.48% control group). Co-adaption led to higher prediction accuracies with time, and we could identify adaptions in feature importances unique to each participant with cervical SCI.Significance. Our findings foster the development of multimodal and adaptive HMIs to allow persons with cervical SCI the intuitive control of assistive devices to improve personal independence.

Effect of muscle length in a handgrip task on corticomotor excitability of extrinsic and intrinsic hand muscles under resting and submaximal contraction conditions

The neurophysiological mechanisms underlying muscle force control for different wrist postures still need to be better understood. To further elucidate these mechanisms, the present study aimed to investigate the effects of wrist posture on the corticospinal excitability by transcranial magnetic stimulation (TMS) of extrinsic (flexor [FCR] and extensor carpi radialis [ECR]) and intrinsic (flexor pollicis brevis (FPB)) muscles at rest and during a submaximal handgrip strength task. Fourteen subjects (24.06 ± 2.28 years) without neurological or motor disorders were included. We assessed how the wrist posture (neutral: 0°; flexed: +45°; extended: -45°) affects maximal handgrip strength (HGSmax ) and the motor evoked potentials (MEP) amplitudes during rest and active muscle contractions. HGSmax was higher at 0° (133%) than at -45° (93.6%; p < 0.001) and +45° (73.9%; p < 0.001). MEP amplitudes were higher for the FCR at +45° (83.6%) than at -45° (45.2%; p = 0.019) and at +45° (156%; p < 0.001) and 0° (146%; p = 0.014) than at -45° (106%) at rest and active condition, respectively. Regarding the ECR, the MEP amplitudes were higher at -45° (113%) than at +45° (60.8%; p < 0.001) and 0° (72.6%; p = 0.008), and at -45° (138%) than +45° (96.7%; p = 0.007) also at rest and active conditions, respectively. In contrast, the FPB did not reveal any difference among wrist postures and conditions. Although extrinsic and intrinsic hand muscles exhibit overlapping cortical representations and partially share the same innervation, they can be modulated differently depending on the biomechanical constraints.

Fine motor assessment in upper extremity using custom-made electronic pegboard test

A fine motor test involves the manipulation of smaller objects with fingers, hands, and wrists. This test is an integral part of the evaluation of an upper extremity function. Nine Hole Peg Test (NHPT) is one among such tests which assess the ability to manipulate pegs with the thumb and finger. There is a need to develop a fine motor assessment tool which is reproducible and mimics closely the natural movement of hands. The aim of this work is to develop an electronic pegboard which is easy to administer and efficient in terms of time. Pegboard device is modified and standardized by (1) Adding electronic circuits to custom-made pegboard and programmed using a microcontroller (ATmega2560), (2) Following a specific sequence in placing and picking the pegs from the board, and (3) Using Infrared sensor and robust algorithm to ensure one peg movement at a time. The setup is administered on 15 healthy participants (nine females, six males aged between 21 and 80) and the outcome is compared with the results of traditional NHPT. Predefined sequence in moving the pegs and electronic timer features provide reliable results for repeated measurements and facilitate storing test score in a digital repository. This data could be used as reference data during the follow-up visits. The maximum difference between the measured timing between the present setup and traditional NHPT is about 6.7%. It is important to note that, due to inherent delay (response time) in the traditional NHPT, when compared to present setup the measured timing is always on the higher side. Nondependency on the manual stopwatch to record the time and hands-free of any wearable device are the advantages of the present setup.