On the Detection of Independent Finger Movements
through Two Forearm Myoelectrodes
P. Steele, J. Brooks, N. Gomes, Y. Sun, E. Chabot
University of Rhode Island
45 Upper College Road
Kingston, RI 02881
Abstract— This study analyses forearm muscle contractions that
occur during movements of individual fingers. An amplitude
threshold is implemented on the multiplied backwards difference
(MOBD) of the signal to determine when a muscle contraction
occurs, and is followed by a windowed variance threshold of the
MOBD to determine which finger contracted. The purpose is to
create a low-cost, computationally efficient method to determine
specific finger contractions from the forearm. This method was
able to differentiate the ring finger from the other fingers, which
in comparison had a variance approximately ten times higher.
I. INTRODUCTION
Approximately 250,000 Americans have a spinal cord
injury, with 12,000 new injuries occurring each year.
Therefore, there is a growing need for assisting these patients,
specifically those with paraplegia. A common method for
increasing the quality of life of individuals with paraplegia is
the power wheelchair. However, a joystick, which is one of
the few readily available mechanisms of control, may not be
feasible for individuals with limited arm mobility due to a
reduced range of motion. Many proposed electromyogram
(EMG) based control methods distinguish between different
contractions including activation site, pattern, and amplitude
leverage complex algorithms[1] and multiple electrodes[2],
the latter two of which make these methods more costly and
intrusive on the user. A new method is presented that utilizes
one differential measurement combined with a multiplication
of backward differences (MOBD) algorithm based upon work
by Suppappola [3] to simplify hardware and software design.
generate one EMG measurement with a third electrode being a
ground reference. The electrodes are placed on the belly of the
flexor carpi radialis, the belly of the palmaris longus, and,
lastly, to the area slightly proximal of the olecranon, serving
as a driven ground. Signal conditioning is performed by
differentially amplifying the electrodes by a gain of 50. Prior
to digitization, the signal is high pass filter at 7 Hz and notch
filtered at 60 Hz. Data is sampled at a rate of 250 Hz.
B. Finger Discrimination Algorithm
With a digitized signal, an algorithm is proposed for the
detection of muscle activation and discrimination of
contracting finger. First, a multiplication of backward
differences (MOBD) algorithm is applied by differencing
sampled data as follows:
𝑥[𝑛] = 𝑢[𝑛] − 𝑢[𝑛 − 1]
where u is the input samples and n is the sample number. If
u[n] and u[n-1] have differing signs, the resultant value of the
difference, x[n], is defined as 0. Next, backward differences,
x[n], are multiplied based on a MOBD order of N=2
𝑁−1
𝑦[𝑛] = ∏ 𝑥[𝑛 − 𝑘]
𝑘=0
Any threshold exceedance of y[n] is defined as an
activation, where the threshold is heuristically set at 0.01V.
Due to the random signal fluctuations, an activation threshold
crossing is maintained for R samples subsequent.
Experimentally, this was defined as 100 samples (or 2/5ths of
a second).
Further extending the process, the variance of the MOBD is
calculated over a moving window of size M as
𝑀−1
1
𝑣[𝑛] = ∑ |𝑥[𝑛 + 𝑖] − 𝜇|2
𝑀
𝑖=0
where 𝜇 is defined as the mean of the windowed samples.
Figure 1. Flow Diagram of Signal
II. METHODS
A. Instrumentation Overview
For the proposed technique, two electrodes are used to
978-1-4799-8360-5/15/$31.00 ©2015 IEEE.
Subsequently, a threshold is applied to the variance to classify
different types of finger based activations over a window. The
window size is selected based up an acceptable
misclassification rate and desired threshold level. A larger
window can reduce the misclassification rate and allow for an
increased variance threshold. Choosing a large window size
also comes at the expense of a decision delay, which reduces
responsiveness of the interface.
III. .RESULTS
Sample data was recorded from each finger by contracting
and releasing the finger, alternating approximately every two
seconds, in a ten second sample period. In figure 2, an
example data set is presented of the smallest finger with the
resultant MOBD value and activation detection result. In
figure 3, the fraction of points above the variance threshold is
illustrated.
2
Raw Signal
MOBD
1.5
On/off Detection
Amplitude
1
Computationally complex algorithms, such as Multiclass
Support Vector Machine (SVM) based algorithms, have been
shown to work with high classification accuracy for each
individual finger activation and robustness to subject variation
[4], but are limited in potential applications due to system
complexity and configuration demands. With advances in
technology, these methods may demonstrate more feasibility
in low-power or long endurance applications.
Further work is anticipated to refine this method by changes
to hardware and algorithm design. While the technique
described could not reliably isolate all fingers, an expansion of
the approach could also be explored with additional electrodes
and modified electrode placement. Even with the addition of
electrodes, the system would likely fit in a low power
microcontroller environment. Additionally, an algorithm for
automatic threshold adjustment based upon the individual
should be explored.
0.5
0.8
Index
0
Middle
0.7
-1
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Sample
Figure 2. This graph represents the original data of the ring finger with
the bias removed (red), the MOBD performed on the data (cyan), and the
detection of a contraction by the variance algorithm (black).
IV. CONCLUSIONS
An MOBD algorithm has been proposed which is
computationally efficient and allows for discrimination of
finger activation. From preliminary data captured, the results
show that muscle activations associated with different finger
movements have distinct statistics. The different distributions
of variance are used to isolate the ring finger from the index,
middle, and smallest fingers. With a threshold of 0.003 on the
variance, the ring finger data reflects greater than 10% of the
active points above the threshold. Correspondingly, all other
fingers don’t have any data points above the threshold
resulting in no false alarms. Based upon the average rate of
points above the threshold, a decision window would need to
be greater than 10 points. A larger window should be selected
to reduce the associated risk of a misclassification trade-off
with the decision delay of the window length.
This simple method can potentially be used in the future to
control assistive technology devices through the use of EMG
with the mapping of each finger to a direction or movement.
For example, contracting the smallest finger could be used to
move forward, contracting the ring finger could be used to
move backwards, and once the middle and index fingers have
been distinguished separately, contracting the middle finger
could be used to move left, and index finger could be used to
move right.
Fraction Above Threshold
Ring
-0.5
Pinky
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.001
0.002
0.003
0.004
0.005 0.006
0.007
0.008
0.009
0.01
Threshold
Figure 3. This graph represents the fraction of points above a threshold.
This illustrates the ability to discriminate between the ring finger and the
other classes. The error bars represent the minimum and maximum
bounds of the individual trials.
ACKNOWLEDGEMENT
This work was fully supported by the University Of Rhode
Island Office Of Research Development through the URI
Undergraduate Research Initiative Program.
REFERENCES
[1] S. Han et al, ‘Human-Machine Interface for wheelchair control with EMG
and its evaluation’, Proceedings of the 25th International Conference of
the IEEE EMBS, Cancun, 2003.
[2] X. Xu et al.,‘Robust Bio-Signal Control of an Intelligent Wheelchair’,
Journal of Robotics, 2013. Print.
[3] S. Suppappola and Ying Sun, 'Nonlinear transforms of ECG signals for
digital QRS detection: a quantitative analysis', IEEE Transactions on
Biomedical Engineering, vol. 41, no. 4, pp. 397-400, 1994.
[4] S. Maier and P. van der Smagt, 'Surface EMG suffices to classify the
motion of each finger independently', in 9th International Conference on
Motion and Vibration Control, Munich, 2008.