Improved sensorimotor adaptation after exhaustive exercise is accompanied by altered brain activity

Acute exercise has been shown to exhibit different effects on human sensorimotor behavior; however, the causes and mechanisms of the responses are often not clear. The primary aim of the present study was to determine the effects of incremental running until exhaustion on sensorimotor performance and adaptation in a tracking task. Subjects were randomly assigned to a running group (RG), a tracking group (TG), or a running followed by tracking group (RTG), with 10 subjects assigned to each group. Treadmill running velocity was initially set at 2.0 m s− 1, increasing by 0.5 m s− 1 every 5 min until exhaustion. Tracking consisted of 35 episodes (each 40 s) where the subjects' task was to track a visual target on a computer screen while the visual feedback was veridical (performance) or left-right reversed (adaptation). Resting electroencephalographic (EEG) activity was recorded before and after each experimental condition (running, tracking, rest). Tracking performance and the final amount of adaptation did not differ between groups. However, task adaptation was significantly faster in RTG compared to TG. In addition, increased alpha and beta power were observed following tracking in TG but not RTG although exhaustive running failed to induce significant changes in these frequency bands. Our results suggest that exhaustive running can facilitate adaptation processes in a manual tracking task. Attenuated cortical activation following tracking in the exercise condition was interpreted to indicate cortical efficiency and exercise-induced facilitation of selective central processes during actual task demands.

Robots move : bootstrapping the development of object representations using sensorimotor coordination

This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.

Comparison between electrically-evoked and voluntary wrist movements on sensorimotor and prefrontal cortical activation : A multi-channel time domain fNIRS study

Neuromuscular electrical stimulation (NMES) has been consistently demonstrated to improve skeletal muscle function in neurological populations with movement disorders, such as poststroke and incomplete spinal cord injury (Vanderthommen and Duchateau, 2007). Recent research has documented that rapid, supraspinal central nervous system reorganisation/neuroplastic mechanisms are also implicated during NMES (Chipchase et al., 2011). Functional neuroimaging studies have shown NMES to activate a network of sub-cortical and cortical brain regions, including the sensorimotor (SMC) and prefrontal (PFC) cortex (Blickenstorfer et al., 2009; Han et al., 2003; Muthalib et al., 2012). A relationship between increase in SMC activation with increasing NMES current intensity up to motor threshold has been previously reported using functional MRI (Smith et al., 2003). However, since clinical neurorehabilitation programmes commonly utilise NMES current intensities above the motor threshold and up to the maximum tolerated current intensity (MTI), limited research has determined the cortical correlates of increasing NMES current intensity at or above MTI (Muthalib et al., 2012). In our previous study (Muthalib et al., 2012), we assessed contralateral PFC activation using 1-channel functional near infrared spectroscopy (fNIRS) during NMES of the elbow flexors by increasing current intensity from motor threshold to greater than MTI and showed a linear relationship between NMES current intensity and the level of PFC activation. However, the relationship between NMES current intensity and activation of the motor cortical network, including SMC and PFC, has not been clarified. Moreover, it is of scientific and clinical relevance to know how NMES affects the central nervous system, especially in comparison to voluntary (VOL) muscle activation. Therefore, the aim of this study was to utilise multi-channel time domain fNIRS to compare SMC and PFC activation between VOL and NMESevoked wrist extension movements.

Sensorimotor and postural control factors associated with driving safety in a community-dwelling older driver population

Background. To establish whether sensorimotor function and balance are associated with on-road driving performance in older adults. Methods. The performance of 270 community-living adults aged 70–88 years recruited via the electoral roll was measured on a battery of peripheral sensation, strength, flexibility, reaction time, and balance tests and on a standardized measure of on-road driving performance. Results. Forty-seven participants (17.4%) were classified as unsafe based on their driving assessment. Unsafe driving was associated with reduced peripheral sensation, lower limb weakness, reduced neck range of motion, slow reaction time, and poor balance in univariate analyses. Multivariate logistic regression analysis identified poor vibration sensitivity, reduced quadriceps strength, and increased sway on a foam surface with eyes closed as significant and independent risk factors for unsafe driving. These variables classified participants into safe and unsafe drivers with a sensitivity of 74% and specificity of 70%. Conclusions. A number of sensorimotor and balance measures were associated with driver safety and the multivariate model comprising measures of sensation, strength, and balance was highly predictive of unsafe driving in this sample. These findings highlight important determinants of driver safety and may assist in developing efficacious driver safety strategies for older drivers.

Developing grounded representations for robots through the principles of sensorimotor coordination

Robots currently recognise and use objects through algorithms that are hand-coded or specifically trained. Such robots can operate in known, structured environments but cannot learn to recognise or use novel objects as they appear. This thesis demonstrates that a robot can develop meaningful object representations by learning the fundamental relationship between action and change in sensory state; the robot learns sensorimotor coordination. Methods based on Markov Decision Processes are experimentally validated on a mobile robot capable of gripping objects, and it is found that object recognition and manipulation can be learnt as an emergent property of sensorimotor coordination.

Precise relative clock synchronization for distributed control using TSC registers

Precise clock synchronization is essential in emerging time-critical distributed control systems operating over computer networks where the clock synchronization requirements are mostly focused on relative clock synchronization and high synchronization precision. Existing clock synchronization techniques such as the Network Time Protocol (NTP) and the IEEE 1588 standard can be difficult to apply to such systems because of the highly precise hardware clocks required, due to network congestion caused by a high frequency of synchronization message transmissions, and high overheads. In response, we present a Time Stamp Counter based precise Relative Clock Synchronization Protocol (TSC-RCSP) for distributed control applications operating over local-area networks (LANs). In our protocol a software clock based on the TSC register, counting CPU cycles, is adopted in the time clients and server. TSC-based clocks offer clients a precise, stable and low-cost clock synchronization solution. Experimental results show that clock precision in the order of 10~microseconds can be achieved in small-scale LAN systems. Such clock precision is much higher than that of a processor's Time-Of-Day clock, and is easily sufficient for most distributed real-time control applications over LANs.

Primary and secondary neural networks of auditory prepulse inhibition: a functional magnetic resonance imaging study of sensorimotor gating of the human acoustic startle response

Feedforward inhibition deficits have been consistently demonstrated in a range of neuropsychiatric conditions using prepulse inhibition (PPI) of the acoustic startle eye-blink reflex when assessing sensorimotor gating. While PPI can be recorded in acutely decerebrated rats, behavioural, pharmacological and psychophysiological studies suggest the involvement of a complex neural network extending from brainstem nuclei to higher order cortical areas. The current functional magnetic resonance imaging study investigated the neural network underlying PPI and its association with electromyographically (EMG) recorded PPI of the acoustic startle eye-blink reflex in 16 healthy volunteers. A sparse imaging design was employed to model signal changes in blood oxygenation level-dependent (BOLD) responses to acoustic startle probes that were preceded by a prepulse at 120 ms or 480 ms stimulus onset asynchrony or without prepulse. Sensorimotor gating was EMG confirmed for the 120-ms prepulse condition, while startle responses in the 480-ms prepulse condition did not differ from startle alone. Multiple regression analysis of BOLD contrasts identified activation in pons, thalamus, caudate nuclei, left angular gyrus and bilaterally in anterior cingulate, associated with EMGrecorded sensorimotor gating. Planned contrasts confirmed increased pons activation for startle alone vs 120-ms prepulse condition, while increased anterior superior frontal gyrus activation was confirmed for the reverse contrast. Our findings are consistent with a primary pontine circuitry of sensorimotor gating that interconnects with inferior parietal, superior temporal, frontal and prefrontal cortices via thalamus and striatum. PPI processes in the prefrontal, frontal and superior temporal cortex were functionally distinct from sensorimotor gating.

Effects of the sensorimotor training volume on sensorimotor function in patients following lower limb arthroplasty

Background Sensorimotor function is degraded in patients after lower limb arthroplasty. Sensorimotor training is thought to improve sensorimotor skills, however, the optimal training stimulus with regard to volume, frequency, duration, and intensity is still unknown. The aim of this study, therefore, was to firstly quantify the progression of sensorimotor function after total hip (THA) or knee (TKA) arthroplasty and, as second step, to evaluate effects of different sensorimotor training volumes. Methods 58 in-patients during their rehabilitation after THA or TKA participated in this prospective cohort study. Sensorimotor function was assessed using a test battery including measures of stabilization capacity, static balance, proprioception, and gait, along with a self-reported pain and function. All participants were randomly assigned to one of three intervention groups performing sensorimotor training two, four, or six times per week. Outcome measures were taken at three instances, at baseline (pre), after 1.5 weeks (mid) and at the conclusion of the 3 week program (post). Results All measurements showed significant improvements over time, with the exception of proprioception and static balance during quiet bipedal stance which showed no significant main effects for time or intervention. There was no significant effect of sensorimotor training volume on any of the outcome measures. Conclusion We were able to quantify improvements in measures of dynamic, but not static, sensorimotor function during the initial three weeks of rehabilitation following TKA/THA. Although sensorimotor improvements were independent of the training volume applied in the current study, long-term effects of sensorimotor training volume need to be investigated to optimize training stimulus recommendations.

Effects of increasing neuromuscular electrical stimulation current intensity on cortical sensorimotor network activation: A time domain fNIRS study

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O₂Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O₂Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O₂Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Small nerve fiber quantification in the diagnosis of diabetic sensorimotor polyneuropathy: Comparing corneal confocal microscopy with intraepidermal nerve fiber density

OBJECTIVE Quantitative assessment of small fiber damage is key to the early diagnosis and assessment of progression or regression of diabetic sensorimotor polyneuropathy (DSPN). Intraepidermal nerve fiber density (IENFD) is the current gold standard, but corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, has the potential to be a noninvasive and objective image biomarker for identifying small fiber damage. The purpose of this study was to determine the diagnostic performance of CCM and IENFD by using the current guidelines as the reference standard. RESEARCH DESIGN AND METHODS Eighty-nine subjects (26 control subjects and 63 patients with type 1 diabetes), with and without DSPN, underwent a detailed assessment of neuropathy, including CCM and skin biopsy. RESULTS Manual and automated corneal nerve fiber density (CNFD) (P < 0.0001), branch density (CNBD) (P < 0.0001) and length (CNFL) (P < 0.0001), and IENFD (P < 0.001) were significantly reduced in patients with diabetes with DSPN compared with control subjects. The area under the receiver operating characteristic curve for identifying DSPN was 0.82 for manual CNFD, 0.80 for automated CNFD, and 0.66 for IENFD, which did not differ significantly (P = 0.14). CONCLUSIONS This study shows comparable diagnostic efficiency between CCM and IENFD, providing further support for the clinical utility of CCM as a surrogate end point for DSPN.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.

Extending life-cycle costing (LCC) analysis for sustainability considerations in road infrastructure projects

Sustainable development is about making societal investments. These investments should be in synchronization with the natural environment, trends of social development, as well as organisational and local economies over a long time span. Traditionally in the eyes of clients, project development will need to produce the required profit margins, with some degrees of consideration for other impacts. This is being changed as all citizens of our society are becoming more aware of concepts and challenges such as the climate change, greenhouse footprints, and social dimensions of sustainability, and will in turn demand answers to these issues in built facilities. A large number of R&D projects have focused on the technical advancement and environmental assessment of products and built facilities. It is equally important address the cost/benefit issue, as developers in the world would not want to loose money by investing in built assets. For infrastructure projects, due to its significant cost of development and lengthy delivery time, presenting the full money story of going green is of vital importance. Traditional views of life-cycle costing tend to focus on the pure economics of a construction project. Sustainability concepts are not broadly integrated with the current LCCA in the construction sector. To rectify this problem, this paper reports on the progress to date of developing and extending contemporary LCCA models in the evaluation of road infrastructure sustainability. The suggested new model development is based on sustainability indicators identified through previous research, and incorporating industry verified cost elements of sustainability measures. The on-going project aims to design and a working model for sustainability life-cycle costing analysis for this type of infrastructure projects.

Contributions of vision-proprioception interactions to the estimation of time-varying hand and target locations

We investigated the relative importance of vision and proprioception in estimating target and hand locations in a dynamic environment. Subjects performed a position estimation task in which a target moved horizontally on a screen at a constant velocity and then disappeared. They were asked to estimate the position of the invisible target under two conditions: passively observing and manually tracking. The tracking trials included three visual conditions with a cursor representing the hand position: always visible, disappearing simultaneously with target disappearance, and always invisible. The target’s invisible displacement was systematically underestimated during passive observation. In active conditions, tracking with the visible cursor significantly decreased the extent of underestimation. Tracking of the invisible target became much more accurate under this condition and was not affected by cursor disappearance. In a second experiment, subjects were asked to judge the position of their unseen hand instead of the target during tracking movements. Invisible hand displacements were also underestimated when compared with the actual displacement. Continuous or brief presentation of the cursor reduced the extent of underestimation. These results suggest that vision–proprioception interactions are critical for representing exact target–hand spatial relationships, and that such sensorimotor representation of hand kinematics serves a cognitive function in predicting target position. We propose a hypothesis that the central nervous system can utilize information derived from proprioception and/or efference copy for sensorimotor prediction of dynamic target and hand positions, but that effective use of this information for conscious estimation requires that it be presented in a form that corresponds to that used for the estimations.