Addressing the challenges posed by human machine interfaces based on force sensitive resistors for powered prostheses

Despite the advancements in the mechatronics aspect of prosthetic devices, prostheses control still lacks an interface that satisfies the needs of the majority of users. The research community has put great effort into the advancements of prostheses control techniques to address users’ needs. However, most of these efforts are focused on the development and assessment of technologies in the controlled environments of laboratories. Such findings do not fully transfer to the daily application of prosthetic systems. The objectives of this thesis focus on factors that affect the use of Force Myography (FMG) controlled prostheses in practical scenarios. The first objective of this thesis assessed the use of FMG as an alternative or synergist Human Machine Interface (HMI) to the more traditional HMI, i.e. surface Electromyography (sEMG). The assessment for this study was conducted in conditions that are relatively close to the real use case of prosthetic applications. The HMI was embedded in the custom prosthetic prototype that was developed for the pilot participant of the study using an off-the-shelf prosthetic end effector. Moreover, prostheses control was assessed as the user moved their limb in a dynamic protocol.The results of the aforementioned study motivated the second objective of this thesis: to investigate the possibility of reducing the complexity of high density FMG systems without sacrificing classification accuracies. This was achieved through a design method that uses a high density FMG apparatus and feature selection to determine the number and location of sensors that can be eliminated without significantly sacrificing the system’s performance. The third objective of this thesis investigated two of the factors that contribute to increased errors in force sensitive resistor (FSR) signals used in FMG controlled prostheses: bending of force sensors and variations in the volume of the residual limb. Two studies were conducted that proposed solutions to mitigate the negative impact of these factors. The incorporation of these solutions into prosthetic devices is discussed in these studies.It was demonstrated that FMG is a promising HMI for prostheses control. The facilitation of pattern recognition with FMG showed potential for intuitive prosthetic control. Moreover, a method for the design of a system that can determine the required number of sensors and their locations on each individual to achieve a simpler system with comparable performance to high density FMG systems was proposed and tested. The effects of the two factors considered in the third objective were determined. It was also demonstrated that the proposed solutions in the studies conducted for this objective can be used to increase the accuracy of signals that are commonly used in FMG controlled prostheses

Bicycle Smart Helmet

Cycle Bright Solutions believe that the current helmets can greatly benefit from the advancements of technology by the creation of a smart helmet. We aim to create a helmet which will increase the safety of riders by making it easier for cars and cyclists to communicate with each other. The conventional hand signals are inadequate at best and downright dangerous at worst. They require the cyclist to remove one hand from the handle bar to perform hand motions that might throw them off balance and even then, they may be not even be feasible at times due to variety of factors such as grade or conditions of the road. Furthermore, these hand signals can be hard to see in low light conditions. To address these issues, the Cycle Bright Solutions’ helmets will feature an RGB LED panel at the back of the helmet that can display left and right signals as well as brake lights to warn the other cars in a timely manner. Thus, the Smart Helmet will help the riders to signal their directions at any time of day without requiring them to remove their hands from the handlebar

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as reported by multiple studies, accuracies of the FSR-based pressure sensing systems decrease when sensors are bent to be positioned on a limb. This study proposes the use of regression-based methods for sensor calibration to address this problem. A sensor matrix was placed in a pressure chamber as the pressure was increased and decreased in a cyclic manner. Sensors’ responses were assessed when the matrix was placed on a flat surface or on one of five curved surfaces with various curvatures. Three regression algorithms, namely linear regression (LR), general regression neural network (GRNN), and random forest (RF), were assessed. GRNN was selected due to its performance. Various error compensation methods using GRNN were investigated and compared to improve instability of sensors’ responses. All methods showed improvements in results compared to the baseline. Developing a different model for each of the curvatures yielded the best results. This study proved the feasibility of using regression-based error compensation methods to improve the accuracy of mapping sensor readings to pressure values. This can improve the overall accuracy of FSR-based sensory systems used in prosthetic applications

Textile-Based Body Capacitive Sensing for Knee Angle Monitoring

Monitoring human movement is highly relevant in mobile health applications. Textile-based wearable solutions have the potential for continuous and unobtrusive monitoring. The precise estimation of joint angles is important in applications such as the prevention of osteoarthritis or in the assessment of the progress of physical rehabilitation. We propose a textile-based wearable device for knee angle estimation through capacitive sensors placed in different locations above the knee and in contact with the skin. We exploited this modality to enhance the baseline value of the capacitive sensors, hence facilitating readout. Moreover, the sensors are fabricated with only one layer of conductive fabric, which facilitates the design and realization of the wearable device. We observed the capability of our system to predict knee sagittal angle in comparison to gold-standard optical motion capture during knee flexion from a seated position and squats: the results showed an R2 coefficient between 0.77 and 0.99, root mean squared errors between 4.15 and 12.19 degrees, and mean absolute errors between 3.28 and 10.34 degrees. Squat movements generally yielded more accurate predictions than knee flexion from a seated position. The combination of the data from multiple sensors resulted in R2 coefficient values of 0.88 or higher. This preliminary work demonstrates the feasibility of the presented system. Future work should include more participants to further assess the accuracy and repeatability in the presence of larger interpersonal variability.ISSN:1424-822

Dataset of Psychological Scales and Physiological Signals Collected for Anxiety Assessment Using a Portable Device

Portable and wearable devices are becoming increasingly common in our daily lives. In this study, we examined the impact of anxiety-inducing videos on biosignals, particularly electrocardiogram (ECG) and respiration (RES) signals, that were collected using a portable device. Two psychological scales (Beck Anxiety Inventory and Hamilton Anxiety Rating Scale) were used to assess overall anxiety before induction. The data were collected at Simon Fraser University from participants aged 18–56, all of whom were healthy at the time. The ECG and RES signals were collected simultaneously while participants continuously watched video clips that stimulated anxiety-inducing (negative experience) and non-anxiety-inducing events (positive experience). The ECG and RES signals were recorded simultaneously at 500 Hz. The final dataset consisted of psychological scores and physiological signals from 19 participants (14 males and 5 females) who watched eight video clips. This dataset can be used to explore the instantaneous relationship between ECG and RES waveforms and anxiety-inducing video clips to uncover and evaluate the latent characteristic information contained in these biosignals

Dataset of Psychological Scales and Physiological Signals Collected for Anxiety Assessment Using a Portable Device

Portable and wearable devices are becoming increasingly common in our daily lives. In this study, we examined the impact of anxiety-inducing videos on biosignals, particularly electrocardiogram (ECG) and respiration (RES) signals, that were collected using a portable device. Two psychological scales (Beck Anxiety Inventory and Hamilton Anxiety Rating Scale) were used to assess overall anxiety before induction. The data were collected at Simon Fraser University from participants aged 18–56, all of whom were healthy at the time. The ECG and RES signals were collected simultaneously while participants continuously watched video clips that stimulated anxiety-inducing (negative experience) and non-anxiety-inducing events (positive experience). The ECG and RES signals were recorded simultaneously at 500 Hz. The final dataset consisted of psychological scores and physiological signals from 19 participants (14 males and 5 females) who watched eight video clips. This dataset can be used to explore the instantaneous relationship between ECG and RES waveforms and anxiety-inducing video clips to uncover and evaluate the latent characteristic information contained in these biosignals

Preliminary investigation of the effect of artificial sweat on a wearable textile sensing system

Introduction Textile wearable systems for human movement monitoring are increasingly popular. However, few examples report on robustness to sweat, which is relevant for use in real life. Some reported the effect of artificial sweat like phosphate buffered saline (PBS; Lin et al., 2022) or simply moisture (Xu et al., 2020) on custom materials. We previously developed an all-textile wireless sensing platform with commercial conductive yarns and fabrics containing silver. There is no study on the effect of sweat on such materials, therefore we performed a preliminary study to account for moisture and potential oxydation of silver. Methods The textile sensing system is resonating RLC circuit, where the sensing part is a capacitive parallel plate strain sensor (C) located on a joint (knee). All components are textile based and contain silver. As the capacitive sensor stretches, capacitance increases and the resonance of the circuit fres decreases. This information is transmitted wirelessly via inductive coupling (Galli et al., 2023). We sprayed 1 ml of 0.1 M PBS solution on the textile capacitive sensor to simulate sweating, and applied mechanical strain before (damp state) and after air drying (dry state). The unmodified sensor (before the addition of any PBS) was also used as a baseline measure. First, we applied fixed strain (10%) with a universal testing machine; then, we tested the response of the sensorized pants when bending the knee. Results The resonance frequency of the textile sensing (RLC) circuit in the damp state was much lower than the baseline (14.85 ± 0.11 MHz vs 22.70 ± 0.12 MHz) as expected from the higher dielectric constant of water that increases the baseline capacitance of the sensor. As for the change in Δfres upon 10% strain (Δfres = fres,baseline - fres,stretch), interestingly a larger change was observed for the damp configuration as compared to the baseline and dried (1.08 ± 0.08 vs 0.79 ± 0.06 vs 0.66 ± 0.03 MHz). A similar behaviour was observed in the test with pants, where the response for flexion was Δfres = 1.58 MHz for the damp sensor and Δfres = 1.28 MHz for the dried sensor. Discussion/Conclusion This preliminary investigation showed promising results in terms of robustness of our system to artificial sweat, as there was a measurable response both in the damp and dried configurations. Further tests with different sweat amounts and rate are needed to determine the full functioning range, e.g., how much sweat is tolerated. References Galli, V., Sailapu, S. K., Cuthbert, T. J., Ahmadizadeh, C., Hannigan, B. C., & Menon, C. (2023). Passive and wireless all-textile wearable sensor system. Advanced Science 10(22), Article 2206665. https://doi.org/10.1002/advs.202206665 Lin, R., Kim, H.-J., Achavananthadith, S., Xiong, Z., Lee, J. K. W., Kong, Y. L., & Ho, J. S. (2022). Digitally-embroidered liquid metal electronic textiles for wearable wireless systems. Nature Communications, 13, Article 2190. https://doi.org/10.1038/s41467-022-29859-4 Xu, L., Liu, Z., Zhai, H., Chen, X., Sun, R., Lyu, S., Fan, Y., Yi, Y., Chen, Z., Jin, L., Zhang, J., Li, Y., & Ye, T. T. (2020). Moisture-resilient graphene-dyed wool fabric for strain sensing. ACS Applied Materials & Interfaces, 12(11), 13265–13274. https://doi.org/10.1021/acsami.9b2096

Inductive Textile Sensing for Movement Monitoring

Textile-based wearable technologies have significant potential for monitoring movements in fitness and health applications. Inductive sensing is a promising modality due to the ease of sensors fabrication and the quality of sensors' response. This study initially focused on designing individual textile inductive sensors. Then, selected parameters were used to create a textile-based garment with inductive sensors capable of monitoring back movements in three directions. The study successfully demonstrated that the developed garment can monitor back movements in three directions.ISSN:2475-147

Distributed sensing along fibers for smart clothing

Textile sensors transform our everyday clothing into a means to track movement and biosignals in a completely unobtrusive way. One major hindrance to the adoption of “smart” clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics: Connections between rigid and textile elements are often unreliable, and they require interfacing sensors in a way incompatible with textile mass production methods. We introduce a prototype garment, compact readout circuit, and algorithm to measure localized strain along multiple regions of a fiber. We use a helical auxetic yarn sensor with tunable sensitivity along its length to selectively respond to strain signals. We demonstrate distributed sensing in clothing, monitoring arm joint angles from a single continuous fiber. Compared to optical motion capture, we achieve around five degrees error in reconstructing shoulder, elbow, and wrist joint angles.ISSN:2375-254

Preliminary investigation of the effect of artificial sweat on a wearable textile sensing system

Introduction Textile wearable systems for human movement monitoring are increasingly popular. However, few examples report on robustness to sweat, which is relevant for use in real life. Some reported the effect of artificial sweat like phosphate buffered saline (PBS; Lin et al., 2022) or simply moisture (Xu et al., 2020) on custom materials. We previously developed an all-textile wireless sensing platform with commercial conductive yarns and fabrics containing silver. There is no study on the effect of sweat on such materials, therefore we performed a preliminary study to account for moisture and potential oxydation of silver. Methods The textile sensing system is resonating RLC circuit, where the sensing part is a capacitive parallel plate strain sensor (C) located on a joint (knee). All components are textile based and contain silver. As the capacitive sensor stretches, capacitance increases and the resonance of the circuit fres decreases. This information is transmitted wirelessly via inductive coupling (Galli et al., 2023). We sprayed 1 ml of 0.1 M PBS solution on the textile capacitive sensor to simulate sweating, and applied mechanical strain before (damp state) and after air drying (dry state). The unmodified sensor (before the addition of any PBS) was also used as a baseline measure. First, we applied fixed strain (10%) with a universal testing machine; then, we tested the response of the sensorized pants when bending the knee. Results The resonance frequency of the textile sensing (RLC) circuit in the damp state was much lower than the baseline (14.85 ± 0.11 MHz vs 22.70 ± 0.12 MHz) as expected from the higher dielectric constant of water that increases the baseline capacitance of the sensor. As for the change in Δfres upon 10% strain (Δfres = fres,baseline - fres,stretch), interestingly a larger change was observed for the damp configuration as compared to the baseline and dried (1.08 ± 0.08 vs 0.79 ± 0.06 vs 0.66 ± 0.03 MHz). A similar behaviour was observed in the test with pants, where the response for flexion was Δfres = 1.58 MHz for the damp sensor and Δfres = 1.28 MHz for the dried sensor. Discussion/Conclusion This preliminary investigation showed promising results in terms of robustness of our system to artificial sweat, as there was a measurable response both in the damp and dried configurations. Further tests with different sweat amounts and rate are needed to determine the full functioning range, e.g., how much sweat is tolerated. References Galli, V., Sailapu, S. K., Cuthbert, T. J., Ahmadizadeh, C., Hannigan, B. C., & Menon, C. (2023). Passive and wireless all-textile wearable sensor system. Advanced Science 10(22), Article 2206665. https://doi.org/10.1002/advs.202206665 Lin, R., Kim, H.-J., Achavananthadith, S., Xiong, Z., Lee, J. K. W., Kong, Y. L., & Ho, J. S. (2022). Digitally-embroidered liquid metal electronic textiles for wearable wireless systems. Nature Communications, 13, Article 2190. https://doi.org/10.1038/s41467-022-29859-4 Xu, L., Liu, Z., Zhai, H., Chen, X., Sun, R., Lyu, S., Fan, Y., Yi, Y., Chen, Z., Jin, L., Zhang, J., Li, Y., & Ye, T. T. (2020). Moisture-resilient graphene-dyed wool fabric for strain sensing. ACS Applied Materials & Interfaces, 12(11), 13265–13274. https://doi.org/10.1021/acsami.9b2096