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Control scheme selection in human-machine- interfaces by analysis of activity signals

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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

Human-Machine Interfaces in rehabilitation engineering often use activity signals. Examples are electrical wheelchairs or prostheses controlled by means of muscle contractions. Activity signals are user-dependent and often reflect neurological weaknesses. Thus, not all users are able to operate the same control scheme in a robust manner. To avoid under- and overstraining, the interface ideally uses a control scheme which reflects the user’s control ability best. Therefore, we explored typical phenomena of activation signals. We derive criteria to quantify the user’s performance and abilities and present a routine which automatically selects and adapts one of three control schemes being best suited.

Keywords: calibration; data quality; human-machine interfaces; rehabilitation engineering

1 Introduction

Human-machine interfaces (HMI) for controlling technical devices in rehabilitation engineering often use electroencephalography (EEG) [1] or electromyography (EMG) [2] to obtain bioelectric signals. Normalization procedures [3] and pattern recognition techniques [4] are used to estimate control signals for devices like electrical wheelchairs [5] or prostheses [6].

A common feature generated from biosignals is the normalized activity signal (e.g. amplitude of a low-pass-filter). Activity signals can also be derived from joystick axes [7], air pressure with sipping and puffing and positions of shoulders, tongue [8] or head [9].

To generate control signals, commercial systems use robust threshold approaches [10], whereas experimental systems use pattern recognition techniques [11]. A calibration is necessary to adapt the system to the user. Commercial HMIs are adapted mostly by hand.

Ref. [12] presents a wheelchair control interface based on the bilateral recording of myoelectric signals from left and right ear muscle. The raw signals are rectified, filtered and normalized to receive activity signals that correlate with the strength of contractions. The ability to contract the ear muscles is trainable and left and right ear muscle can be activated independently. However, not only training progress is user-dependent, but also extrema and coactivations of the activation signals, dispersion of the intended constant activations, difference between measured and intended activation.

As the control abilities of users vary, users might not only need adapted parameters but also individual control schemes. However, there is only little knowledge about user-specific selections of the control scheme.

Therefore, we propose a method to select a control scheme based on activity signals from a calibration routine. We derive a rule set to assign each user one out of three control schemes. Using benchmark examples we test the method and discuss effects. We provide a real-world dataset to prove functionality.

2 Material and methods

2.1 Calibration routine

If a user is not able to activate two signal channels independently, coactivations occur and control schemes based on difference signals fail. Polynomial regressions approximate the nonlinear relationship of intended activation and measured coactivated signals. Therefore, bilateral calibration routines were proposed in [13], [14] to gather data for model building: The user is asked to simultaneously hold activation levels in two signal channels. The activation levels are the intended activations (intentions) y = (y1, y2). The calibration routine records the activation of each channel according to Table 1 for

Table 1

Intended activations and classes of the calibration steps.

Calibration stepy1y2z
1: single x110B1
2: single x1 (50%)0.50B2
3: single x201B3
4: single x2 (50%)00.5B4
5: simultaneous x1 and x211B5
6: simultaneous x1 and x2 (50%)0.50.5B6

  • single maximal activations of each channel,

  • simultaneous maximal activation of both channels,

  • single activations of each channel at 50% activation level and

  • simultaneous activations of both channels at 50% activation level.

Each of the six calibration steps is described by the membership to a class z = {B1, ⋯, B6} and a vector of intentions y = (y1, y2). The user-generated normalized activation signals are x1 and x2. Table 1 shows the calibration steps, the class membership and intended activations.

2.2 Selection of a control scheme

A control scheme estimates intentions by thresholds or pattern recognition algorithms. We consider three different control schemes:

A threshold-based control using one signal channel to recognize intentions given by short/long activations. Sequences comparable to the Morse code are assigned to actions of the rehabilitation device (e.g. turning left or right with a wheelchair). The advantage of the control scheme is its simplicity. Even with coactivations and low abilities of holding an activation level this control scheme is applicable. Disadvantages are time lags in control.

If the user is able to generate better discriminable activation patterns a classifier-based control can be applied. A classifier = f(x1, x2; θ) is trained for the 6 classes Bi within a calibration B1, ⋯, B6 and classifies the signals at each time sample. Classes represent intentions, the control scheme maps each intention to an action. No activation in both channels corresponds to a neutral state. Inputs of the classifier are the normalized activation signals x1 and x2. The parameter vector θ is estimated based on calibration data.

If the user is able to hold different activation levels with both signal channels, a proportional control can be applied. It determines two independent, time-continuous control signals by the level of simultaneous activation of both channels and the difference of both channels (e.g. translational velocity and rotational velocity of a wheelchair). To reduce the influence of unintended coactivations, regression models ŷi = fi(x1, x2; θi) are used to estimate the continuously valued intention of each channel [13] with the constraints f1(0, 0, θ1) = f2(0, 0, θ2) = 0. f1 and f2 are polynomial functions of second degree and the parameter vectors θ1 and θ2 are estimated by a least squares algorithm. Fluctuating activity signals lead to incessantly changing velocities in the proportional control.

To select an appropriate control scheme three criteria are defined which are also useful for a medical supervisor:

Q1 describes the ability of the user to generate discriminable activation signals for the classifier-based control scheme. Therefore, we calculate the minimal accuracy of a class for the used classification algorithm

(1)Q1=minj((1Nj⋅N(z^=Bj∩z=Bj)).

Q2 quantifies whether the generated signals match the intended activations and describes the ability of the user to generate predetermined activation signals. For a robust estimation of the mean value, the median operator is used. x~j, i is the median of the xi values of all datapoints with class label Bj. yj, i is the intended activation for channel i of all datapoints with class label Bj:

(2)Q2=exp (−4⁢(maxj,i (x~j,i−yj,i)))

The factor −4 is selected empirically to achieve a smooth course of the criterion. Q3 quantifies the dispersion of the generated signals. It describes the ability of the user to hold an activation level. For a robust estimation of the dispersion the interquartile range is used. x~0.75, j, i is the upper quartile und x~0.25, j, i the lower quartile of the xi values of all datapoints with class label Bj:

(3)Q3=exp (−4⁢(maxj,i (x~0.75,j,i−x~0.25,j,i)))

(4) shows the rules for the selection of an appropriate control scheme.

(4)(Q1>τ1)∧(Q2>τ2)∧(Q3>τ3)→proportional(Q1>τ1)∧¬((Q2>τ2)∧(Q3>τ3))→classifier¬ (Q1>τ1)→threshold

The thresholds τ1, τ2 and τ3 are selected empirically.

2.3 Datasets

We simulated three benchmark datasets which represent three states of user performance: Figure 1(A) shows the calibration data of a user with low performance. The data of the calibration steps is overlapping and the dispersion of single classes is high. Figure 1(B) shows the calibration data of a user with medium performance. The dispersion of the classes is high but the classes are discriminable. Figure 1(C) shows the calibration data of a user with high performance. The datapoints of the calibration steps are building small clusters near to their intended activations.

Figure 1 (A)-(C): Simulated datasets representing different levels of user performance. (D)-(F): Parts of the real-world datasets from [14]. Only the calibration steps are used which would have been recorded from the proposed calibration routine.
Figure 1

(A)-(C): Simulated datasets representing different levels of user performance. (D)-(F): Parts of the real-world datasets from [14]. Only the calibration steps are used which would have been recorded from the proposed calibration routine.

In [14] real-world datasets in a bilateral calibration routine were recorded with antagonistic forearm muscles. Three datasets are generated by one user with varying sensor placements for 12 intentions. Figure 1(D–F) show subsets of the datasets. The subsets correspond to the intended activations of Table 1. The data is preprocessed, i.e. outliers of the calibration steps were already deleted.

3 Results

Analyses were performed with MATLAB and Gait-CAD [15]. Table 2 shows Q1, Q2, Q3 and the control scheme selection for the simulated and the real-world datasets with τ1 = 0.9, τ2 = 0.15 and τ3 = 0.5. The thresholds are empirically selected.

Table 2

Results for simulated (1–3) and real-world (4–6) benchmark datasets. Values below τ-thresholds are bold.

DatasetQ1Q2Q3Selection
10.5560.66720.2222threshold
20.9560.22070.3065classifier
310.30050.6583proportional
410.0640.7579classifier
510.03720.5945classifier
610.17420.6995proportional

The assessment by the criteria conforms to the description of the benchmark datasets. For dataset 1, Q1 and Q3 are below their respective thresholds. Thus, the calibration steps are not discriminable and the dispersion of at least one calibration step is high. The threshold-based control scheme is selected. This evaluation coincides with the visual inspection of dataset 1. With the threshold-based control scheme, the user is at least able to apply several movements even if it takes time. In a classifier-based or a proportional control scheme, the estimated intentions are fluctuating due to the high dispersion in classes and the overlap of different classes. Also for dataset 2, Q3 is below its threshold. Since the calibration steps are discriminable, the classifier-based control scheme is selected. In this control scheme, the user is able to hold a movement. In the proportional control scheme the velocities are fluctuating due to the high dispersion in classes. For dataset 3, all criteria are above their thresholds. The proportional control scheme is selected.

The real-world datasets look similar to dataset 3 which is appropriate for the proportional control scheme. But the proximity of calibration steps (e.g. B4 and B6 in dataset 4 or B1 and B5 in dataset 5) leads to a rapid change of velocities by small fluctuations of activation signals in a proportional control scheme. The calibration steps of the real-world datasets 4, 5 and 6 are discriminable, their dispersions are low and so Q1 and Q3 are above their thresholds. For datasets 4 and 5, Q2 is below its thresholds. That means the data of the calibration steps is different to the intended signals and the proportional control scheme is not appropriate for the user. Instead the classifier-based control scheme is selected. Only for dataset 6 the proportional control scheme is selected.

4 Discussion and outlook

The automatic control scheme selection helps to find a control scheme that fits to the user’s performance. Thus, frustration of the user by under- or overstraining can be avoided. The interpretability of the proposed criteria as user performance allows the use in studies and medical investigations. The user performance over several calibrations can be followed and changes in neurological and physiological weaknesses can be quantified. Without our criteria, the information about user performance is lost by using the data just for training a model to estimate intended activations.

The proposed criteria are able to represent different phenomena of activation signal calibration data. The usage of the criteria for selecting an appropriate control scheme has been demonstrated with the help of three simulated and three real-world datasets. The method is currently used to prepare a study regarding user performance evaluation.

Author’s Statement

Research funding: The project is funded by the Helmholtz Association (Program bioInterfaces in technology and medicine (RM, MR) and the German Federal Ministry of Education and Research BMBF [WD, DL, RR,MR, Grant No 13EZ1122(A–C)]. Conflict of interest: Authors state no conflict of interest. Informed consent has been obtained from all individuals included in this study. Ethical approval: The research related to human use complies with all the relevant national regulations, institutional policies and in accordance the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board.

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Markus Reischl et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0153
Keywords for this article
calibration; data quality; human-machine interfaces; rehabilitation engineering
Creative Commons
BY-NC-ND 4.0

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  54. Real-time QRS detection using integrated variance for ECG gated cardiac MRI
  55. Preprocessing of unipolar signals acquired by a novel intracardiac mapping system
  56. In-vitro experiments to characterize ventricular electromechanics
  57. Continuous non-invasive monitoring of blood pressure in the operating room: a cuffless optical technology at the fingertip
  58. Application of microwave sensor technology in cardiovascular disease for plaque detection
  59. Artificial blood circulatory and special Ultrasound Doppler probes for detecting and sizing gaseous embolism
  60. Detection of microsleep events in a car driving simulation study using electrocardiographic features
  61. A method to determine the kink resistance of stents and stent delivery systems according to international standards
  62. Comparison of stented bifurcation and straight vessel 3D-simulation with a prior simulated velocity profile inlet
  63. Transient Euler-Lagrange/DEM simulation of stent thrombosis
  64. Automated control of the laser welding process of heart valve scaffolds
  65. Automation of a test bench for accessing the bendability of electrospun vascular grafts
  66. Influence of storage conditions on the release of growth factors in platelet-rich blood derivatives
  67. Cryopreservation of cells using defined serum-free cryoprotective agents
  68. New bioreactor vessel for tissue engineering of human nasal septal chondrocytes
  69. Determination of the membrane hydraulic permeability of MSCs
  70. Climate retainment in carbon dioxide incubators
  71. Multiple factors influencing OR ventilation system effectiveness
  72. Evaluation of an app-based stress protocol
  73. Medication process in Styrian hospitals
  74. Control tower to surgical theater
  75. Development of a skull phantom for the assessment of implant X-ray visibility
  76. Surgical navigation with QR codes
  77. Investigation of the pressure gradient of embolic protection devices
  78. Computer assistance in femoral derotation osteotomy: a bottom-up approach
  79. Automatic depth scanning system for 3D infrared thermography
  80. A service for monitoring the quality of intraoperative cone beam CT images
  81. Resectoscope with an easy to use twist mechanism for improved handling
  82. In vitro simulation of distribution processes following intramuscular injection
  83. Adjusting inkjet printhead parameters to deposit drugs into micro-sized reservoirs
  84. A flexible standalone system with integrated sensor feedback for multi-pad electrode FES of the hand
  85. Smart control for functional electrical stimulation with optimal pulse intensity
  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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