The bipedal saddle space: modelling and validation.

Better understanding of human balance control is pivotal for applications such as bipedal robots and medical technologies/therapies targeting human locomotion. Despite the inverted pendulum model being popular for describing bipedal locomotion, it does not properly capture the step-to-step transition dynamics. The major drawback has been the requirement for both feet to be on the ground which generates a discontinuity along the intersection of the potential energy surfaces produced by the two legs. To overcome this problem, we propose a generalized inverted pendulum-based model that can describe both single and double support phases. The full characterization of the system's potential energy allows the proposed model to drop the main limitation. This framework also enables optimal strategies to be designed for the transition between the two feet without the optimization algorithms. The proposed theory has been validated by comparing the human locomotor strategies output of our planner with real data from multiple experimental studies. The results show that our model generates trajectories consistent with human variability and performs better than existing well-known methods.

Identification of emotion associated brain functional network with phase locking value.

Recognition of discriminative brain functional network pattern and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Emotion models mapping onto brain is possible with the help of emotion-specific network patterns and its corresponding brain regions. This paper presents a method to identify emotion related functional connectivity pattern and their distinctive associated regions using EEG phase synchrony (phase locking value (PLV)) connectivity analysis. The emotion-specific channel pairs, reactive band, and synchrony related locations are identified based on the network dissimilarities between emotion and rest tasks. With the most reactive pairs identified, the emotion-specific functional network is formed. The proposed method is validated on `database for emotion analysis using physiological signals (DEAP)' that confirms the distinct nature of identified functional connectivity pattern and the regions corresponding to the emotion.

Spatial filter and feature selection optimization based on EA for multi-channel EEG.

The EEG signals employed for BCI systems are generally band-limited. The band-limited multiple Fourier linear combiner (BMFLC) with Kalman filter was developed to obtain amplitude estimates of the EEG signal in a pre-fixed frequency band in real-time. However, the high-dimensionality of the feature vector caused by the application of BMFLC to multi-channel EEG based BCI deteriorates the performance of the classifier. In this work, we apply evolutionary algorithm (EA) to tackle this problem. The real-valued EA encodes both the spatial filter and the feature selection into its solution and optimizes it with respect to the classification error. Three BMFLC based BCI configurations are proposed. Our results show that the BMFLC-KF with covariance matrix adaptation evolution strategy (CMAES) has the best overall performance.

EEG classification of emotions using emotion-specific brain functional network.

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

Adaptive super-twisting observer for estimation of random road excitation profile in automotive suspension systems.

The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system.

Multi-step prediction of physiological tremor for robotics applications.

The performance of surgical robotic devices in real-time mainly depends on phase-delay in sensors and filtering process. A phase delay of 16-20 ms is unavoidable in these robotics procedures due to the presence of hardware low pass filter in sensors and pre-filtering required in later stages of cancellation. To overcome this phase delay, we employ multi-step prediction with band limited multiple Fourier linear combiner (BMFLC) and Autoregressive (AR) methods. Results show that the overall accuracy is improved by 60% for tremor estimation compared to single-step prediction methods in the presence of phase delay. Experimental results with the proposed methods for 1-DOF tremor estimation highlight the improvement.

Online LS-SVM based multi-step prediction of physiological tremor for surgical robotics.

Performance of robotics based hand-held surgical devices in real-time is mainly dependent on accurate filtering of physiological tremor. The presence of phase delay in sensors (hardware) and filtering (software) processes affects the cancellation accuracy. This paper focuses on developing an estimation algorithm to improve the estimation accuracy in the presence of phase delay for real-time implementations. Moving window based online training approach for least squares-support vector machines (LSSVM) is employed in this paper for tremor estimation. A study is conducted with tremor data recorded from the subjects to analyze the suitability of proposed approach for both single-step and multi-step prediction.

Phase synchrony in subject-specific reactive band of EEG for classification of motor imagery tasks.

Recent works on brain functional analysis have highlighted the importance of distributed functional networks and synchronized activity between networks in mediating cognitive functions. The network perspective is fundamental to relate mechanisms of brain functions and the basis for classifying brain states. This work analyzes the network mechanisms related to motor imagery tasks based on synchronization measure (PLV (phase-locking value)) in EEG alpha-band for the BCI Competition IV Data Set. Based on network dissimilarities between motor imagery and rest tasks, important nodes and important channel pairs corresponding to tasks for all subjects are identified. The identified important channel pairs corresponding to tasks demonstrate significant PLV variation in line with the experiment protocol. With the selection of subject-specific reactive band, these channel pairs provide even more higher variation corresponding to tasks. This paper demonstrates the potential of these identified channel pairs in task classification for future BCI applications.

Estimation and filtering of physiological tremor for real-time compensation in surgical robotics applications.

BACKGROUND: Physiological tremor is the main cause of imprecision in microsurgical procedures/robotics applications. Existing methods, such as weighted-frequency Fourier linear combiner (WFLC), rely on estimating the tremor under the assumption that it has a single dominant frequency. This paper focuses on developing a new algorithm for accurate tremor filtering in real time. METHODS: A study conducted on several novice subjects and microsurgeons showed the tremor to contain several dominant frequencies in a band, rather than a single dominant frequency. Based on the tremor characteristics, a new algorithm band-limited multiple Fourier linear combiner (BMFLC) has been developed to estimate a band of signals with multiple dominant frequencies. A separation procedure to separate the intended motion/drift from the tremor portion is also discussed. RESULTS: A simulation study was first conducted to validate the theoretical development on recorded tremor data. The experimental set-up was designed to study the real-time performance of the proposed algorithm. Tremor sensing using accelerometers is also discussed, with the proposed algorithm. Our experiments showed that the developed BMFLC algorithm had an average tremor compensation of 64% compared to 43% for the WFLC algorithm in real-time for one degree of freedom (1-DOF) cancellation of tremor. CONCLUSIONS: The BMFLC algorithm can be applied for the three axes separately and 3-DOF cancellation of tremor can be achieved. Further research is required to deal with complex gestures involved during microsurgery.

Design and implementation of a two degree-of-freedom micromanipulation assessment system.

A two degree-of-freedom (DOF) micro motion sensing system to assess micromanipulation accuracy and physiological tremor has been developed. The system employs a position sensitive detector (PSD) module and a laser diode placed inside an instrument used in micromanipulation. A laser light is shined from the laser diode onto the PSD surface which is faced upward. The PSD detects the centroid position of the laser light spot falling onto its surface. A few markers are overlaid in a circular pattern on the PSD surface as reference for path dependent tests. Ambient light disturbance is eliminated by modulating the laser light on and off. Advantages and limitation of the system comparing to other similar systems are described.

A study of a hand-held instrument's angular motion due to physiological tremor in micromanipulation tasks.

Angular motion of a hand-held instrument due to physiological tremor during micromanipulation tasks was recorded with a six degree-of-freedom accelerometer-based sensing unit placed in the instrument. Methods to get angular velocities and angular accelerations from the acceleration readings of accelerators in the sensing unit are described. Statistics of angular velocity and angular acceleration of the instrument due to the tremor obtained from ten normal subjects are reported. Assumption of very small tremor angular velocities in micromanipulation tasks to calculate tremor angular accelerations analytically is validated.

System to assess accuracy of micromanipulation.

The authors had previously developed an optical micro motion sensing system (M2S2) using a pair of orthogonally placed position sensitive detectors (PSD) to track 3D displacement of the tip of a microsurgical instrument in real-time. In the M2S2 system, an infrared (IR) diode is used to illuminate the workspace. A ball is attached to the tip of an intraocular shaft to reflect IR rays onto the PSDs. Instrument tip position is then calculated from the centroid position of reflected IR light on the PSDs. The M2S2 system together with a test platform is used as an evaluation system to assess the accuracy and physiological tremor of subjects performing micromanipulation tasks. Since the need to use the ball at the instrument tip prevents the subjects from performing the manipulation tasks precisely, a laser light is provided as a guide for the subjects to aim at the target precisely. A laser diode module is placed inside the instrument to provide the required laser light. The instrument intraocular shaft is replaced with a same-sized hollow tube to let the laser light from the instrument pass through down to the target. The laser light spot position on the platform is used to access the performance of the subjects. The laser light spot position is calculated from the tilt angle information provided by an accelerometer placed inside the instrument, and the instrument tip position information given by M2S2.

Bandlimited multiple Fourier linear combiner for real-time tremor compensation.

Surgical accuracy of the hand-held instruments depends on the active compensation of disturbance and tremor. Physiological tremor is one of the main causes for imprecision in micro-surgery procedures. One of the popular tremor compensation methods is based on weighted-frequency Fourier linear combiner (WFLC) algorithm, that can adapt to the changes in frequency as well as amplitude of the tremor signal. WLFC estimates the dominant frequency and the amplitude. For the case of tremor with frequency variation or comprising of two or three frequencies close in spectral domain, the WFLC performance is degraded. In this paper, we present a bandlimited multiple Fourier linear combiner that can track the modulated signals with multiple frequency components. We also discuss the tremor sensing with accelerometers. Using the proposed algorithm the drift caused by the accelerometers is also eliminated. The proposed filter is tested in real-time for 1-DOF cancellation of tremor.