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. 2017 Dec 6:4:2055668317746307.
doi: 10.1177/2055668317746307. eCollection 2017 Jan-Dec.

Wearable step counting using a force myography-based ankle strap

Kelvin Ht Chu  1 , Xianta Jiang  1 , Carlo Menon  1
Affiliations

Wearable step counting using a force myography-based ankle strap

Kelvin Ht Chu et al. J Rehabil Assist Technol Eng. .

Abstract

Introduction: Step counting can be used to estimate the activity level of people in daily life; however, commercially available accelerometer-based step counters have shown inaccuracies in detection of low-speed walking steps (<2.2 km/h), and thus are not suitable for older adults who usually walk at low speeds. This proof-of-concept study explores the feasibility of using force myography recorded at the ankle to detect low-speed steps.

Methods: Eight young healthy participants walked on a treadmill at three speeds (1, 1.5, and 2.0 km/h) while their force myography signals were recorded at the ankle using a customized strap embedded with an array of eight force-sensing resistors. A K-nearest neighbour model was trained and tested with the recorded data. Additional three mainstream machine learning algorithms were also employed to evaluate the performance of force myography band as a pedometer.

Results: Results showed a low error rate of the step detection (<1.5%) at all three walking speeds.

Conclusions: This study demonstrates not only the feasibility of the proposed approach but also the potential of the investigated technology to reliably monitor low-speed step counting.

Keywords: Force myography; force-sensing resistor band; gait analysis; older adults; pedometer.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Experiment set-up. (a) The FSR band composed of a Bluetooth module, Arduino pro mini, 3.5 V battery and eight FSRs, (b) the FSR band mounted firmly at the ankle position, and (c) two FSRs attached on the bottom of heel and toe for labelling when the foot is in the air or the ground. FSR: force-sensing resistor.
Figure 2.
Figure 2.
Examples of raw and normalized FSR signals. (a) A segment of four-step raw FSR signal from subject 1, trial 4, speed 2 and (b) the normalized FSR signal from the same segment signal of panel A, where each trial was normalized using the maximum and minimum values of signals of the trial. FSR: force-sensing resistor.
Figure 3.
Figure 3.
The FSR signal from the two labelling FSRs (FSRheel and FSRtoe) and the labels (orange) (subject 1, trial 4, speed 2) after applying threshold. FSR: force-sensing resistor.
Figure 4.
Figure 4.
Definition of the start and end of swing/stance phase (step). A step includes a stance phase and a swing phase. The light-blue circles are the threshold labels indicating the data in swing or stance phases, which are measured from the signals from the underfoot labelling FSRs.
Figure 5.
Figure 5.
Step filtering process to remove noisy steps. (a) Predicted label – without filtering (subject 8, trial 1, speed 2) shows the unfiltered steps, (b) Predicted label – with stance filtering shows the result using a four-sample threshold, and (c) Predicted label – with swing filtering shows the result using a two-sample threshold.
Figure 6.
Figure 6.
Example of true and predicted step labels (subject 7, trial 3, speed 2). The blue circle shows one true step is predicted as two steps (false positive) and the red circle shows a true positive step.
Figure 7.
Figure 7.
(a) Sample-based error and (b) step-count error rates for all three speeds across eight subjects. The error bars are 1 standard deviation. KNN: K-nearest neighbour; LDA: linear discriminant analysis; NN: neural network; SVM: support vector machine.
Figure 8.
Figure 8.
The confusion matrix of all 15 trials showing the sample-based error using the KNN classifier. The darkness of each cell in the matrix is the percentage of true samples (in y-axis) that had been predicted as the class in x-axis.
Figure 9.
Figure 9.
The averaged sample-based error corresponds to the number of nearest neighbours across all eight subjects. KNN: K-nearest neighbour.
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