A physical activity (PA) recognition algorithm for a wearable wireless sensor network using both ambulatory electrocardiogram (ECG) and accelerometer signals is proposed. First, in the time domain, the cardiac activity mean and the motion artifact noise of the ECG signal are modeled by a Hermite polynomial expansion and principal component analysis, respectively. A set of time domain accelerometer features is also extracted. A support vector machine (SVM) is employed for supervised classification using these time domain features. Second, motivated by their potential for handling convolutional noise, cepstral features extracted from ECG and accelerometer signals based on a frame level analysis are modeled using Gaussian mixture models (GMMs). Third, to reduce the dimension of the tri-axial accelerometer cepstral features which are concatenated and fused at the feature level, heteroscedastic linear discriminant analysis is performed. Finally, to improve the overall recognition performance, fusion of the multi-modal (ECG and accelerometer) and multidomain (time domain SVM and cepstral domain GMM) subsystems at the score level is performed. The classification accuracy ranges from 79.3% to 97.3% for various testing scenarios and outperforms the state-of-the-art single accelerometer based PA recognition system by over 24% relative error reduction on our nine-category PA database.
Accelerometer; cepstrum; electrocardiogram; multimodal signal processing; physical activity recognition
Targeted Reinnervation is a surgical technique developed to increase the number of myoelectric input sites available to control an upper-limb prosthesis. Because signals from the nerves related to specific movements are used to control those missing degrees-of-freedom, the control of a prosthesis using this procedure is more physiologically appropriate compared to conventional control. This procedure has successfully been performed on three people with a shoulder disarticulation level amputation and three people with a transhumeral level amputation. Performance on timed tests, including the box-and-blocks test and clothespin test, has increased two to six times. Options for new control strategies are discussed.
Arm Prosthesis; Artificial Limbs; Bionic; Electromyography
Targeted muscle reinnervation (TMR) is a novel neural machine interface for improved myoelectric prosthesis control. Previous high-density (HD) surface electromyography (EMG) studies have indicated that tremendous neural control information can be extracted from the reinnervated muscles by EMG pattern recognition (PR). However, using a large number of EMG electrodes hinders clinical application of the TMR technique. This study investigated a reduced number of electrodes and the placement required to extract sufficient neural control information for accurate identification of user movement intents. An electrode selection algorithm was applied to the HD EMG recordings from each of 4 TMR amputee subjects. The results show that when using only 12 selected bipolar electrodes the average accuracy over subjects for classifying 16 movement intents was 93.0(±3.3)%, just 1.2% lower than when using the entire HD electrode complement. The locations of selected electrodes were consistent with the anatomical reinnervation sites. Additionally, a practical protocol for clinical electrode placement was developed, which does not rely on complex HD EMG experiment and analysis while maintaining a classification accuracy of 88.7±4.5%. These outcomes provide important guidelines for practical electrode placement that can promote future clinical application of TMR and EMG PR in the control of multifunctional prostheses.
Targeted muscle reinnervation; EMG; clinical EMG electrode configurations; control of artificial limbs; prosthesis; classification
The lack of proprioceptive feedback is a serious deficiency of current prosthetic control systems. The Osseo-Magnetic Link (OML) is a novel humeral or wrist rotation control system that could preserve proprioception. It utilizes a magnet implanted within the residual bone and sensors mounted in the prosthetic socket to detect magnetic field vectors and determine the bone's orientation. This allows the use of volitional bone rotation to control a prosthetic rotator. We evaluated the performance of the OML using a physical model of a transhumeral residual limb. A small Neodymium-Iron-Boron magnet was placed in a model humerus, inside a model upper arm. Four 3-axis Hall-effect sensors were mounted on a ring 3 cm distal to the magnet. An optimization algorithm based on Newton's method determined the position and orientation of the magnet within the model humerus under various conditions, including bone translations, interference, and magnet misalignment. The orientation of the model humerus was determined within 3° for rotations centered in the arm; an additional 6° error was found for translations 20 mm from center. Adjustments in sensor placement may reduce these errors. The results demonstrate that the OML is a feasible solution for providing prosthesis rotation control while preserving rotational proprioception.
Lower-limb amputees expend more energy to walk than non-amputees and have an elevated risk of secondary disabilities. Insufficient push-off by the prosthetic foot may be a contributing factor. We aimed to systematically study the effect of prosthetic foot mechanics on gait, to gain insight into fundamental prosthetic design principles. We varied a single parameter in isolation, the energy-storing spring in a prototype prosthetic foot, the Controlled Energy Storage and Return (CESR) foot, and observed the effect on gait. Subjects walked on the CESR foot with three different springs. We performed parallel studies on amputees and on non-amputees wearing prosthetic simulators. In both groups, spring characteristics similarly affected ankle and body center-of-mass (COM) mechanics and metabolic cost. Softer springs led to greater energy storage, energy return and prosthetic limb COM push-off work. But metabolic energy expenditure was lowest with a spring of intermediate stiffness, suggesting biomechanical disadvantages to the softest spring despite its greater push-off. Disadvantages of the softest spring may include excessive heel displacements and COM collision losses. We also observed some differences in joint kinetics between amputees and non-amputees walking on the prototype foot. During prosthetic push-off, amputees exhibited reduced energy transfer from the prosthesis to the COM along with increased hip work, perhaps due to greater energy dissipation at the knee. Nevertheless, the results indicate that spring compliance can contribute to push-off, but with biomechanical trade-offs that limit the degree to which greater push-off might improve walking economy.
prosthetic feet; amputee gait; prosthetic simulator; ankle push-off
Pattern recognition–based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01). Real-time controllability was evaluated with the Target Achievement Control (TAC) Test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p<0.01) and was reduced with longer controller delay (p<0.01), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multi-state amplitude controllers.
Controller delay; myoelectric control; pattern recognition; prosthesis; surface electromyography
To investigate the feasibility of game-based robotic training of the ankle in children with cerebral palsy (CP).
Case study, 12 weeks intervention, with no follow-up.
University research laboratory.
A referred sample of 3 children with cerebral palsy, age 7 to 12, all male were enrolled. Three completed the intervention.
Participants trained on the RA CP system for 36 rehabilitation sessions (12 weeks, 3 times/week), playing two custom virtual reality games. The games were played while participants were seated, and trained one ankle at-a-time for strength, motor control, and coordination.
Main Outcome Measures
The primary study outcome measures were for impairment (DF/PF torques, DF initial contact angle and gait speed), function (GMFM) and quality of life (Peds QL). Secondary outcome measures relate to game performance (game scores as reflective of ankle motor control and endurance).
Gait function improved substantially in ankle kinematics, speed and endurance. Overall function (GMFM) indicated improvements that were typical of other ankle strength training programs. Quality of life increased beyond what would be considered a minimal clinical important difference. Game performance improved in both games during the intervention.
This feasibility study supports the assumption that game-based robotic training of the ankle benefits gait in children with CP. Game technology is appropriate for the age group and was well accepted by the participants. Additional studies are needed however, to quantify the level of benefit and compare the approach presented here to traditional methods of therapy.
ankle; cerebral palsy; gait; robotics; video games
A common way for understanding sensory integration in postural control is to provide sinusoidal perturbations to the sensory systems involved in balance. However, not all subjects exhibit a response to the perturbation. Determining whether or not a response has occurred is usually done qualitatively, e.g. by visual inspection of the power spectrum. In this paper we present the application of a statistical test for quantifying whether or not a postural sway response is present. The test uses an F-statistic for determining if there is significant power in postural sway data at the stimulus frequency. In order to describe the application of this method, twenty subjects viewed sinusoidal anterior-posterior optic flow at 0.1 and 0.25 Hz. while their anterior-posterior head translation was measured. The test showed that significant postural responses were detected at the stimulus frequency in 12/20 subjects at 0.1 Hz and 13/20 subjects at 0.25 Hz.
balance; vision; somatosensory; vestibular; signal processing
No adequate treatment exists for individuals who remain disabled by bilateral loss of vestibular (inner ear inertial) sensation despite rehabilitation. We have restored vestibular reflexes using lab-built multichannel vestibular prostheses (MVPs) in animals, but translation to clinical practice may be best accomplished by modification of a commercially available cochlear implant (CI).
We developed software and circuitry to sense head rotation and drive a CI's implanted stimulator (IS) to deliver up to 1Kpulses/s via 9 electrodes implanted near vestibular nerve branches. Studies in two rhesus monkeys using the modified CI (MCI) revealed in vivo performance similar to our existing dedicated MVPs.
Like commercially available CIs, our design uses an external head-worn unit (HWU) that is magnetically coupled across the scalp to the IS. The HWU must remain securely fixed to the skull to faithfully sense head motion with gyroscopes and maintain continuous stimulation. We measured normal and shear force thresholds at which HWU-IS decoupling occurred as a function of scalp thickness and calculated pressure exerted on the scalp. The HWU remained attached across the human scalp thicknesses from 3mm to 7.8mm for forces experienced during routine daily activities, with magnets exerting pressure on the scalp that remains below capillary perfusion pressure.
Electrical stimulation; cochlear implant; neural; prosthesis; bilateral vestibular hypofunction
Variability in severity and progression of Parkinson’s disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinson’s disease.
Exercise; motor function; movement disorders; neurorehabilitation; signal processing
Assessment of network connectivity across multiple brain regions is critical to understanding the mechanisms underlying various neurological disorders. Conventional methods for assessing dynamic interactions include cross-correlation and coherence analysis. However, these methods do not reveal the direction of information flow, which is important for studying the highly directional neurological system. Granger causality (GC) analysis can characterize the directional influences between two systems. We tested GC analysis for its capability to capture directional interactions within both simulated and in-vivo neural networks. The simulated networks consisted of Hindmarsh-Rose neurons; GC analysis was used to estimate the causal influences between two model networks. Our analysis successfully detected asymmetrical interactions between these networks (p<10−10, t-test). Next, we characterized the relationship between the “electrical synaptic strength” in the model networks and interactions estimated by GC analysis. We demonstrated the novel application of GC to monitor interactions between thalamic and cortical neurons following ischemia induced brain injury in a rat model of cardiac arrest (CA). We observed that during the post-CA acute period the GC interactions from the thalamus to the cortex were consistently higher than those from the cortex to the thalamus (1.983±0.278 times higher, p=0.021). In addition, the dynamics of GC interactions between the thalamus and the cortex were frequency dependent. Our study demonstrated the feasibility of GC to monitor the dynamics of thalamocortical interactions after a global nervous system injury such as CA-induced ischemia, and offers preferred alternative applications in characterizing other inter-regional interactions in an injured brain.
Cardiac Arrest; Granger Causality; Local Field Potentials; Network Connectivity; Thalamocortical Network
Intracortical brain computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user’s ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called MOCA, was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors (10.6 ±10.1%; p<0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs.
Brain-computer interfaces (BCI); brain-machine interfaces (BMI); Kalman filter; motor cortex; neural decoding; adaptive filtering
To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.
Brain-computer interface (BCI); Brain-machine interface (BMI); Electrocorticography (ECoG); Hybrid BCI; Intelligent robotics; Intracranial EEG (iEEG)
Trunk torque is typically quantified in a single plane. This is not ideal since any unmeasured coupled trunk kinetics in other directions, than the intended one, could affect the accuracy and reliability of the strength measurements as well as the ability to corroborate findings with electromyographic recordings. Therefore, an isometric device that simultaneously records trunk kinetics across planes has been developed to aid in the research of trunk control in both the healthy or impaired populations. This device utilizes a six degree-of-freedom load cell and custom designed frame to attach individuals while in the sitting position. The performance of the device was tested in six healthy controls and while using two protocols. The device was able to detect coupled trunk kinetics during maximum lateral flexion and axial twisting torque generation. It also allowed the implementation of a multi-axis isometric protocol showing that subjects were able to generate larger amounts of axial torque during sub-maximal trunk extension compared to sub-maximal trunk flexion. In conclusion, the device and mechanical transformations discussed in this article will aid in the interpretation of multi-directional isometric trunk kinetics in a wide range of populations.
Isometric; torque; trunk
While effective in treating some neurological disorders, neuroelectric prostheses are fundamentally limited because they must employ charge-balanced stimuli to avoid evolution of irreversible electrochemical reactions and their byproducts at the interface between metal electrodes and body fluids. Charge-balancing is typically achieved by using brief biphasic alternating current (AC) pulses, which typically excite nearby neural tissues but cannot efficiently inhibit them. In contrast, direct current (DC) applied via a metal electrode in contact with body fluids can excite, inhibit and modulate sensitivity of neurons; however, DC stimulation is biologically unsafe because it violates “safe charge injection” limits that have long been considered unavoidable constraints. In this report, we describe the design and fabrication of a safe DC stimulator (SDCS) that overcomes this constraint. The SCDS drives DC ionic current into target tissue via salt-bridge micropipette electrodes by switching valves in phase with AC square waves applied to metal electrodes contained within the device. This approach achieves DC ionic flow through tissue while still adhering to charge-balancing constraints at each electrode-saline interface. We show the SDCS’s ability to both inhibit and excite neural activity to achieve improved dynamic range during prosthetic stimulation of the vestibular part of the inner ear in chinchillas.
Intracranial electroencephalographic (iEEG) signals from two human subjects were used to achieve simultaneous neural control of reaching and grasping movements with the Johns Hopkins University Applied Physics Lab (JHU/APL) Modular Prosthetic Limb (MPL), a dexterous robotic prosthetic arm. We performed functional mapping of high gamma activity while the subject made reaching and grasping movements to identify task-selective electrodes. Independent, online control of reaching and grasping was then achieved using high gamma activity from a small subset of electrodes with a model trained on short blocks of reaching and grasping with no further adaptation. Classification accuracy did not decline (p<0.05, one-way ANOVA) over three blocks of testing in either subject. Mean classification accuracy during independently executed overt reach and grasp movements for (Subject 1, Subject 2) were (0.85, 0.81) and (0.80, 0.96) respectively, and during simultaneous execution they were (0.83, 0.88) and (0.58, 0.88) respectively. Our models leveraged knowledge of the subject's individual functional neuroanatomy for reaching and grasping movements, allowing rapid acquisition of control in a time-sensitive clinical setting. We demonstrate the potential feasibility of verifying functionally meaningful iEEG-based control of the MPL prior to chronic implantation, during which additional capabilities of the MPL might be exploited with further training.
Brain-machine interface; upper limb prosthesis; electrocorticography; high gamma; functional mapping
Training with haptic guidance has been proposed as a technique for learning complex movements in rehabilitation and sports, but it is unclear how to best deliver guidance-based training. Here, we hypothesized that breaking down a complex movement, similar to a tennis backhand, into simpler parts and then using haptic feedback from a robotic exoskeleton would help the motor system learn the movement. We also examined how the particular form of the decomposition affected learning. Three groups of unimpaired participants trained with the target arm movement broken down in three ways: 1) elbow flexion/extension and the unified shoulder motion independently (“anatomical” decomposition), 2) three component shoulder motions in Euler coordinates and elbow flexion/extension (“Euler” decomposition), or 3) the motion of the tip of the elbow and motion of the hand with respect to the elbow, independently (“visual” decomposition). A control group practiced the same number of movements, but experienced the target motion only, achieving eight times more direct practice with this motion. Despite less experience with the target motion, part training was better, but only when the arm trajectory was decomposed into anatomical components. Varying robotic movement training to include practice of simpler, anatomically-isolated motions may enhance its efficacy.
Haptic arm exoskeleton; motor learning; parallel mechanism; robot assisted movements; whole-part practice
In this study, we developed a robust subject-specific electromyography (EMG) pattern classification technique to discriminate intended manual tasks from muscle activation patterns of stroke survivors. These classifications will enable volitional control of assistive devices, thereby improving their functionality. Twenty subjects with chronic hemiparesis participated in the study. Subjects were instructed to perform six functional tasks while their muscle activation patterns were recorded by ten surface electrodes placed on the forearm and hand of the impaired limb. In order to identify intended functional tasks, a pattern classifier using linear discriminant analysis was applied to the EMG feature vectors. The classification accuracy was mainly affected by the impairment level of the subject. Mean classification accuracy was 71.3% for moderately impaired subjects (Chedoke Stage of Hand 4 and 5), and 37.9% for severely impaired subjects (Chedoke Stage of Hand 2 and 3). Most misclassification occurred between grip tasks of similar nature, for example, among pinch, key, and three-fingered grips, or between cylindrical and spherical grips. EMG signals from the intrinsic hand muscles significantly contributed to the inter-task variability of the feature vectors, as assessed by the inter-task squared Euclidean distance, thereby indicating the importance of intrinsic hand muscles in functional manual tasks. This study demonstrated the feasibility of the EMG pattern classification technique to discern the intent of stroke survivors. Future work should concentrate on the construction of a subject-specific EMG classification paradigm that carefully considers both functional and physiological impairment characteristics of each subject in the target task selection and electrode placement procedures.
Stroke; Electromyography (EMG); Pattern classification; Hand; Functional task
Non-invasive brain stimulation is one of very few potential therapies for medically refractory epilepsy. However, its efficacy remains suboptimal and its therapeutic value has not been consistently assessed. This is in part due to the non-optimized spatio-temporal application of stimulation protocols for seizure prevention or arrest, and incomplete knowledge of the neurodynamics of seizure evolution. Through simulations, this study investigated EEG-guided, stochastic interference with aberrantly coordinated neuronal networks, to prevent seizure onset or interrupt a propagating partial seizure, and prevent it from spreading to large areas of the brain. Brain stimulation was modeled as additive white or band-limited noise, and simulations using real EEGs and data generated from a network of integrate-and-fire neuronal ensembles were used to quantify spatio-temporal noise effects. It was shown that additive stochastic signals (noise) may destructively interfere with network dynamics and decrease or abolish synchronization associated with progressively coupled networks. Furthermore, stimulation parameters, particularly amplitude and spatio-temporal application, may be optimized based on patient-specific neurodynamics estimated directly from non-invasive EEGs.
Non-invasive brain stimulation; epilepsy; EEG; noise; destructive interference
Epilepsy affects approximately one percent of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We have been developing a noninvasive transcranial focal electrical stimulation with our novel tripolar concentric ring electrodes as an alternative/complementary therapy for seizure control. In this study we demonstrate the effect of focal stimulation on behavioral seizure activity induced by two successive pentylenetetrazole administrations in rats. Seizure onset latency, time of the first behavioral change, duration of seizure, and maximal seizure severity score were studied and compared for focal stimulation treated (n = 9) and control groups (n = 10). First, we demonstrate that no significant difference was found in behavioral activity for focal stimulation treated and control groups after the first pentylenetetrazole administration. Next, comparing first and second pentylenetetrazole administrations, we demonstrate there was a significant change in behavioral activity (time of the first behavioral change) in both groups that was not related to focal stimulation. Finally, we demonstrate focal stimulation provoking a significant change in seizure onset latency, duration of seizure, and maximal seizure severity score. We believe that these results, combined with our previous reports, suggest that transcranial focal stimulation may have an anticonvulsant effect.
Epilepsy; pentylenetetrazole; noninvasive transcranial focal electrical stimulation; tripolar concentric ring electrode; seizure
The decomposition of multiunit signals consists of the restoration of spike trains and action potentials in neural or muscular recordings. Because of the complexity of automatic decomposition, semiautomatic procedures are sometimes chosen. The main difficulty in automatic decomposition is the resolution of temporally overlapped potentials. In a previous study , we proposed a Bayesian model coupled with a maximum a posteriori (MAP) estimator for fully automatic decomposition of multiunit recordings and we showed applications to intramuscular EMG signals. In this study, we propose a more complex signal model that includes the variability in amplitude of each unit potential. Moreover, we propose the Markov Chain Monte Carlo (MCMC) simulation and a Bayesian minimum mean square error (MMSE) estimator by averaging on samples that converge in distribution to the joint posterior law. We prove the convergence property of this approach mathematically and we test the method representatively on intramuscular multiunit recordings. The results showed that its average accuracy in spike identification is greater than 90% for intramuscular signals with up to 8 concurrently active units. In addition to intramuscular signals, the method can be applied for spike sorting of other types of multiunit recordings.
Adult; Algorithms; Bayes Theorem; Computer Simulation; Electromyography; Evoked Potentials; physiology; Humans; Male; Markov Chains; Models, Statistical; Monte Carlo Method; Muscle, Skeletal; physiology; Reproducibility of Results; Signal Processing, Computer-Assisted; instrumentation; Stochastic Processes; Young Adult; Bayesian model; MMSE estimation; Markov chain Monte Carlo; intramuscular EMG decomposition
Over the last 20 years, cochlear implants (CIs) have become what is arguably the most successful neural prosthesis to date. Despite this success, a significant number of CI recipients experience marginal hearing restoration, and, even among the best performers, restoration to normal fidelity is rare. In this article, we present image processing techniques that can be used to detect, for the first time, the positions of implanted CI electrodes and the nerves they stimulate for individual CI users. These techniques permit development of new, customized CI stimulation strategies. We present one such strategy and show that it leads to significant hearing improvement in an experiment conducted with 11 CI recipients. These results indicate that image-guidance can be used to improve hearing outcomes for many existing CI recipients without requiring additional surgical procedures.
auditory nerve; cochlear implant; image-guidance; spiral ganglion; stimulation strategy
External feedback of performance is an important component of therapy, especially for children with impairments due to cerebral palsy because they lack intrinsic experience of “good movements” to compare effort and determine performance outcomes. A robotic therapy system was developed to provide feedback for specific upper extremity movements (gestures) which are therapeutically desirable. The purpose of this study was to compare changes in forearm supination/pronation or wrist extension/flexion motion following conventional therapy and gestural robotic feedback therapy intervention. Six subjects with cerebral palsy (ages 5–18, GMFCS level IV—three subjects, level III—one subject, and level I—two subjects) participated in a blinded crossover design study of conventional and robotic feedback therapy targeting either forearm supination or wrist extension. Functional upper extremity motion at baseline and following conventional and robotic feedback therapy interventions were obtained using a motion capture system by personnel blinded to the intervention order. All activities were approved by IRB. Use of the robotic feedback system did result in slightly increased movement in the targeted gesture without change in un-targeted motions. Data also suggest a decrease in both agonist and antagonist motion following conventional therapy intervention. Results suggest improved motion when robotic feedback therapy intervention precedes conventional therapy intervention. Robotic feedback therapy is no different than conventional therapy to improve supination or wrist extension function in upper extremity impairments of children with cerebral palsy when changes were considered as aggregate data. In this very small group of diverse patients, individual subject results suggested that intervention order could be responsible for obscuring differences due to intervention type. Outcomes from several individual subjects suggest that results could be different given a more homogeneous group of subjects which future studies should be considered to ultimately determine efficacy of the robotic feedback therapy. Future studies should also address efficacy in other neuromuscular patient populations.
Cerebral palsy (CP); movement feedback; robotic feedback; upper extremity