Healthcare Robotics

Healthcare Robotics: Upper Limb Prostheses

?

Abstract

This essay describes the robotic systems in healthcare with a focus on upper limb prostheses. The general categories of healthcare robotics are introduced with examples about the tasks they can perform. Prostheses, as an example of rehabilitation robots, are explained with a demonstration of the working principle of the different types available today. The analysis part of this essay discusses the advantages and drawbacks of these prosthetic systems and their best application areas to give an overview of the usage scenarios of the different types of prostheses and explain the trade-off between accuracy, complexity, and affordability. Furthermore, feedback sensory systems’ concept and their effects on the performance of these prosthetic systems are explored with the goal of evaluating the state-of-the-art prostheses and their impact on amputees’ lives. The conclusion suggests that each type of prostheses can perform well in certain ranges depending on the case conditions.

Introduction

Healthcare sector has been evolving in leaps and bounds as the needs are increasing significantly with the new challenges in this sector. Today’s technological advancements are playing a crucial role in response to these challenges, and robotics development is well known for being part of healthcare evolution. Robotics technology in healthcare field can be categorized into three main domains: clinical, assistive and rehabilitation robotics[1]. Clinical robotics involves systems that support medical processes such as surgery and diagnosis. These robots can perform diagnostics, treatments, surgical intervention, and medication to help health workers achieve better results and increased performance. Assistive robotics domain cover systems that assist carers or patients in task-specific processes including logistics, surveillance, waste disposal, bed transfers, etc. lastly, rehabilitation robotics field includes various forms of post-operative or post-injury care systems that interacts directly and physically with the patient to enhance recovery or replace a lost function. Prosthetics, which are external devices that partially or completely replace a lost limb, has come a long way in evolution as rehabilitation robotics starting from cosmetic prosthetics to body powered and finally leading to the most recent advanced prosthetics that use myoelectric bio signals or even further with the advancements of the brain–computer interfacing and neuroscience[2].?

Statistics today show that more than one million annual limb amputations are carried out globally due to accidents, war casualties, diseases, or tumors. There are over 57 million amputees worldwide which made robotic prosthetic limbs a very active field in research and industry[3]. This research area integrates advanced mechatronics, intelligent sensing, and control for achieving higher order lost sensorimotor functions while preserving the physical appearance of the native limb to help amputees win back their normal life and live with less limitations.

Passive prostheses are cosmetic parts that are used to restore the native limb appearance and provide limited functionality without the need to variable control while body-powered prostheses incorporate a mechanical system to transform the motions of other parts of the body to open or close a hand to perform the required task[2]. However, these prostheses do not involve robotic control elements therefore the focus of this essay will be on electrically powered upper limb prostheses.

Electrically-Powered Upper Limb Prostheses

Externally powered prostheses involve a control system that utilizes an input signal (generated by a sensor and/or button) to achieve a movement through actuators system. one of the most common control systems is the myoelectric one, which exploits the electromyographic (EMG) signals of the muscles to create a control signal for the actuators to perform a specific movement corresponding to the EMG signals pattern. There are two different techniques to acquire the EMG signals from the muscles, invasive in which wire or needle electrodes are implanted inside the residual muscles and non-invasive in which skin surface electrodes are used to measure the potential difference of the electrochemical reaction due to the muscle extension and flexion[6].

In the invasive method, the prosthetic system is anchored to the bone at the amputation stump using implant system that is consisted of two mechanical parts: the fixture, a screw made of a specific metal that becomes incorporated into the bone, and the abutment, which is placed inside the fixture and extended out of the body. The electrodes are implanted in the nerves and muscles directly and interfaced with processing unit using embedded electrical connectors[7].

On the other hand, the non-invasive technique utilizes surface electrodes to measure the electric muscle activity and consequently detect the patterns associated with the movements to generate a control signal for actuators.

The first step of the pattern recognition control of prosthetics, is the pre-processing of the different EMG input signals. Usually, this process involves amplification and noise removal. Next, feature extraction methods are carried out in the time and frequency domain to enhance information about EMG contraction in selected time windows. These features are then used to train a classifier which will detect the different patterns corresponding to different movements and in turn, generates the required control signal for the actuators.

Electroencephalography Prostheses

Electroencephalography (EEG) is a brain signal that has been widely used in brain machine interface applications. Electroencephalography is the measurement of the electric brain activity produced by the currents induced by neurons within the brain. A non-invasive way can be used to acquire EEG signals through surface scalp electrodes[8]. The user is often required to either focus on a certain task or provided with an external stimulation (e.g., visual, auditory) to induce brain response. When someone is thinking of a certain action, the brain cells will emit electrochemical impulses with different frequencies that can be recorded by?electroencephalogram. The main EEG rhythms are classified based on the frequency range as alpha, beta, delta, theta, and gamma. For instance, alpha waves are emitted with frequencies ranging from 8 to 13 Hertz during a normal wakeful state where the person is resting[9]. The recording of these waves can be converted to a control signal to move the prosthetic limb through several phases. Firstly, a preprocessing phase is carried out to remove the noise and amplify EEG signals for better recognition. Next, useful features are extracted from the acquired EEG signals using different methods such as wavelet transform and auto regressive method. Lastly, a classification step is performed to detect the signals pattern and each class is associated with a specific command to control the actuators.

?

Hybrid Prostheses

Combining multiple physiological signals is a possible approach to acquire abundant neural information for prosthesis control. A hybrid classification scheme of EEG and EMG can be a solution to provide multiple Degrees of Freedom (DOF) control for prostheses used in transhumeral or even higher degree amputation cases[10]. Depending on the level of amputation, EMG from the remaining muscle groups can be used to provide myoelectric control to the associated DOF while EEG can be used to develop control for the rest of the limb parts and associated DOF. The EMG and EEG signals in this approach are processed independently and then combined to provide the output to the actuators. For example, EEG signals are used to select the gripping pattern while EMG signals are used to activate the gripping action using a threshold-based classifier. On the other hand, a combination of EEG and EMG can be used to control the same DOF to achieve higher accuracy than single-signal approach[11]. In this approach the EEG and EMG is recorded simultaneously when the amputee is asked to focus on performing certain movements for a certain time. EEG and EMG signals are then recorded to be filtered and segmented into a series of analysis windows with a certain length. For each window, time domain features are extracted and used to train the classifier that is used for prediction. At the end, each class is linked to a certain movement that will be triggered when the classifier detects the corresponding pattern.

?

Prostheses Feedback Sensory System

Upper-limb amputees consider the sensory feedback as one of the main missing features in commercial prostheses[12]. Sensory feedback restoration helps amputees to perceive their environment interactions and the actual movement of the artificial limb. This ability can be achieved by replicating the mammal’s somatosensory system, in which internal or external stimuli is transduced by receptors in the skin or muscles into electrical signals that are sent to the brain[13]. Glove or sock with a sensors system is added to the prosthesis to measure the pressure applied to the prosthesis and the movement information of the joints. The outputs of these sensors are transduced by a controller into electrical stimulation parameters through encoding algorithms between artificial and natural sensory signals. The stimulation signals are then delivered to a stimulator system which induces the sensations perceived by the prosthesis user.

On the other hand, haptic feedback is another approach to provide the missing sensory feedback system to the prosthesis[14]. Electrotactile non-invasive stimulation is one of the haptic feedback approaches which involves applying an electrical current over the skin to stimulate sensory nerves. Different sensations (e.g., vibration, touch, and pain) can be achieved in this method by varying the waveform, frequency, or location. Furthermore, the Mechanotactile haptic feedback system incorporate vibration motors or actuated pins to provide feedback sensations to the prosthesis user[15]. These systems provide limited levels of sensations by varying the actuation power according to the intensity of the stimulus.

Discussion

In myoelectric prosthetics, implantable muscle electrodes in invasive approaches have advantages over the skin-surface ones in terms of size, reliability due to permanent location, and skin deformation impact[17]. However, traditional bone-anchored devices require surgical operations which have high rate of infection and chronic inflammation[18]. In addition, these devices require long-term maintenance and have limited affordability due to the cost and complexity of the surgical operations. Controversy, skin-surface methods are safe and easy since no surgery is required which makes it a cheaper option but less accurate in addition to regular calibration requirements for these devices. ????????

Although Myoelectric approaches provide reliable and accurate performance in prosthesis control, they are limited to certain amputation levels where significant amount of the residual limb must be present. To overcome these limitations, EEG control systems were used in severe extremities loss. These methods allow amputees with complete limb loss to win back some of their limbs functionality using their direct brain signals. EEG signals are non-invasive, low cost, compatible, portable and have a high temporal resolution in comparison with other brainwave measurements such as electrocorticograms (ECoGs)[19]. However, these systems provide limited accuracy due to the interference of other brain signals that are coupled with the motor signals, weakness of these signals, proneness to several artifacts, and relatively poor spatial resolution[11].

Alternatively, combining EEG and EMG signals can provide higher accuracy, more complex movements and additional Degrees Of Freedom for severe amputations. The single signal processing approach can increase the number of joints that the amputee can control by using each signal for different joints while the coupled approach provides higher accuracy. However, these systems require extra sensors and processing units to achieve these features which reflects on the cost of these devices and their portability as a commercial product for end users.

?

Although the aforementioned techniques could restore part of the native limb functionality, they cannot execute precise grip and fine manipulations[12]. Therefore, adding a sensory feedback system will give the amputee the ability to achieve sophisticated movements and grasping actions through the information from the environment and proprioception. It should be mentioned that mechanical stimulation approach of these feedback systems causes unavoidable delay (400ms) in sensation whereas electrotactile method involves extensive training for the patient before being able to use these sensations properly[20].

?

Conclusion

The exponential evolution of the Robotics systems is transforming industries worldwide. In?a?clinical?setting,?health?robotics?can?enhance?patient care,?speed?up?procedures, and?provide?a safe environment for?patients and health?professionals. Various prostheses were discussed with the goal to explore their robotics system structure and their applications. The myoelectric control outperform the EEG based devices in terms of accuracy and robustness, but they are limited to a certain level of amputations and can be costly especially the invasive options which require surgical operations. On the other hand, EEG systems can perform well in some cases where most of the limb is missing and for lower costs than EMG systems. Moreover, coupling EMG and EEG signals increases the system accuracy significantly and can offer more degrees of freedom for more complex tasks but as a trade-off with portability and simplicity. Lastly, sensory feedback systems are crucial part of the smart prosthesis to bring back native-like sensations and allow performing excellent grasping and manipulation abilities. In conclusion, each type of prostheses has its application range and limitations and the decision of which to use depends on the amputation levels and system affordability for that case.

References

[1] The Partnership for Robotics in Europe SPARC. Robotics 2020 Multi-Annual Roadmap; Call 2 ICT24

(2015)—Horizon 2020; SPARC: Brussels, Belgium, 2015

[2] Mendez, V., Iberite, F., Shokur, S., & Micera, S. (2021). Current Solutions and Future Trends for Robotic Prosthetic Hands. Annual Review of Control, Robotics, and Autonomous Systems, 4(1), 595-627. doi:10.1146/annurev-control-071020-104336

[3] McDonald, C. L., Westcott-McCoy, S., Weaver, M. R., Haagsma, J., & Kartin, D. (2021). Global prevalence of traumatic non-fatal limb amputation.?Prosthetics and orthotics international,?45(2), 105–114. https://doi.org/10.1177/0309364620972258

[4] Hussain, S., Shams, S., & Khan, S. J. (2019). Impact of Medical Advancement: Prostheses. In L. Wang, & L. Yu (Eds.), Computer Architecture in Industrial, Biomechanical and Biomedical Engineering. IntechOpen. https://doi.org/10.5772/intechopen.86602

[5] Close-up photos of Hands holding artificial arms. [Photograph]. Retrieved March 22, 2022, from: https://www.imeche.org/news/news-article/feature-%27the-price-of-shoes-rather-than-a-car%27-mitt-offers-amputees-a-helping-hand

[6] Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T. J., Naik, G. R., Gunawardana, U., & Gargiulo, G. D. (2019). Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation. Sensors (Basel, Switzerland), 19(20), 4596. https://doi.org/10.3390/s19204596

[7] Al-Ajam, Y., Lancashire, H., Pendegrass, C., Kang, N., Dowling, R. P., Taylor, S. J. G., & Blunn, G. (2013). The Use of a Bone-Anchored Device as a Hard-Wired Conduit for Transmitting EMG Signals From Implanted Muscle Electrodes. IEEE Transactions on Biomedical Engineering, 60(6), 1654–1659. https://doi.org/10.1109/TBME.2013.2241060

[8] Bandara, D., Arata, J., & Kiguchi, K. (2018). Towards Control of a Transhumeral Prosthesis with EEG Signals. Bioengineering (Basel, Switzerland), 5(2), 26. https://doi.org/10.3390/bioengineering5020026

[9] Xavier, G., Su Ting, A., & Fauzan, N. (2020). Exploratory study of brain waves and corresponding brain regions of fatigue on‐call doctors using quantitative electroencephalogram. Journal of Occupational Health, 62(1). https://doi.org/10.1002/1348-9585.12121

[10] Bakshi, K., Pramanik, R., Manjunatha, M., & Kumar, C. S. (2018). Upper Limb Prosthesis Control: A Hybrid EEG-EMG Scheme for Motion Estimation in Transhumeral Subjects. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018, 2024–2027. https://doi.org/10.1109/EMBC.2018.8512678

[11] Li, X., Samuel, O. W., Zhang, X., Wang, H., Fang, P., & Li, G. (2017). A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees. Journal of neuroengineering and rehabilitation, 14(1), 2. https://doi.org/10.1186/s12984-016-0212-z

[12] Biddiss, E., Beaton, D., & Chau, T. (2007). Consumer design priorities for upper limb prosthetics. Disability and Rehabilitation: Assistive Technology, 2(6), 346–357. https://doi.org/10.1080/17483100701714733

[13] Raspopovic, S., Valle, G., & Petrini, F. M. (2021). Sensory feedback for limb prostheses in amputees. Nature Materials, 20(7), 925–939. https://doi.org/10.1038/s41563-021-00966-9

[14] Sensinger, J. W., & Dosen, S. (2020). A Review of Sensory Feedback in Upper-Limb Prostheses From the Perspective of Human Motor Control. Frontiers in neuroscience, 14, 345. https://doi.org/10.3389/fnins.2020.00345

[15] Moore, C. H., Corbin, S. F., Mayr, R., Shockley, K., Silva, P. L., & Lorenz, T. (2021). Grasping Embodiment: Haptic Feedback for Artificial Limbs. Frontiers in neurorobotics, 15, 662397. https://doi.org/10.3389/fnbot.2021.662397

[16] Osborn L, Kaliki R, Soares A, Thakor N. Neuromimetic Event-Based Detection for Closed-Loop Tactile Feedback Control of Upper Limb Prostheses. IEEE Transactions on Haptics. 2016 Apr-Jun;9(2):196-206. DOI: 10.1109/toh.2016.2564965. PMID: 27777640; PMCID: PMC5074548.

[17] Farrell, T. R., & Weir, R. F. (2008). A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control. IEEE transactions on bio-medical engineering, 55(9), 2198–2211. https://doi.org/10.1109/TBME.2008.923917

[18] Li, Y., & Fell?nder-Tsai, L. (2021). The bone anchored prostheses for amputees – Historical development, current status, and future aspects. Biomaterials, 273, 120836. https://doi.org/10.1016/j.biomaterials.2021.120836

[19] Burle, B., Spieser, L., Roger, C., Casini, L., Hasbroucq, T., & Vidal, F. (2015). Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view. International journal of psychophysiology, 97(3), 210–220. https://doi.org/10.1016/j.ijpsycho.2015.05.004

[20] Clemente, F., D’Alonzo, M., Controzzi, M., Edin, B. B., & Cipriani, C. (2016). Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(12), 1314–1322. https://doi.org/10.1109/tnsre.2015.2500586