Identification Of Hand Postures By Force Myography Using An Optical Fiber Specklegram Sensor

The identification of hand postures based on force myography (FMG) measurements using a fiber specklegram sensor is reported. The microbending transducers were attached to the user forearm in order to detect the radial forces due to hand movements, and the normalized intensity inner products of output specklegrams were computed with reference to calibration positions. The correlation between measured specklegrams and postures was carried out by artificial neural networks, resulting in an overall accuracy of 91.3% on the retrieval of hand configuration.963

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Updated Perspectives on the Role of Biomechanics in COPD: Considerations for the Clinician

Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline and their muscle activity is differential from controls. When walking, patients with COPD appear to adopt a gait pattern that may increase stability (eg, shorter and wider steps, decreased gait speed) in addition to altered gait variability. Biomechanical muscle mechanics (ie, tension, extensibility, elasticity, and irritability) alterations with COPD are not well documented, with relatively few articles investigating these properties. On the other hand, dyssynchronous motion of the abdomen and rib cage while breathing is well documented in patients with COPD. Newer biomechanical technologies have allowed for estimation of regional, compartmental, lung volumes during activity such as exercise, as well as respiratory muscle activation during breathing. Future directions of biomechanical analyses in COPD are trending toward wearable sensors, big data, and cloud computing. Each of these offers unique opportunities as well as challenges. Advanced analytics of sensor data can offer insight into the health of a system by quantifying complexity or fluctuations in patterns of movement, as healthy systems demonstrate flexibility and are thus adaptable to changing conditions. Biomechanics may offer clinical utility in prediction of 30-day readmissions, identifying disease severity, and patient monitoring. Biomechanics is complementary to other assessments, capturing what patients do, as well as their capability

Design of a low-cost sensor matrix for use in human-machine interactions on the basis of myographic information

Myographic sensor matrices in the field of human-machine interfaces are often poorly developed and not pushing the limits in terms of a high spatial resolution. Many studies use sensor matrices as a tool to access myographic data for intention prediction algorithms regardless of the human anatomy and used sensor principles. The necessity for more sophisticated sensor matrices in the field of myographic human-machine interfaces is essential, and the community already called out for new sensor solutions. This work follows the neuromechanics of the human and designs customized sensor principles to acquire the occurring phenomena. Three low-cost sensor modalities Electromyography, Mechanomyography, and Force Myography) were developed in a miniaturized size and tested in a pre-evaluation study. All three sensors comprise the characteristic myographic information of its modality. Based on the pre-evaluated sensors, a sensor matrix with 32 exchangeable and high-density sensor modules was designed. The sensor matrix can be applied around the human limbs and takes the human anatomy into account. A data transmission protocol was customized for interfacing the sensor matrix to the periphery with reduced wiring. The designed sensor matrix offers high-density and multimodal myographic information for the field of human-machine interfaces. Especially the fields of prosthetics and telepresence can benefit from the higher spatial resolution of the sensor matrix

Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis

Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputees’ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness. Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all users’ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputees’ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjects’ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks. Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience. Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice

Use of wavelet analysis techniques with surface EMG and MMG to characterise motor unit recruitment patterns of shoulder muscles during wheelchair propulsion and voluntary contraction tasks

The high demand on the upper extremity during manual wheelchair use contributes to a high prevalence of shoulder pathology in people with spinal cord injury. The overall purpose of this thesis was to investigate shoulder muscle recruitment patterns and wheelchair kinetics in able-bodied participants over a range of daily activities and mobility tasks requiring manual wheelchair propulsion. With a complete understanding of the muscle recruitment patterns, physiotherapists and wheelchair users can improve rehabilitation protocols and wheelchair propulsion performance to prevent shoulder pathology and maintain comfort during locomotion. Motor unit recruitment patterns were examined first during isometric and isotonic contractions to determine if spectral properties from EMG and MMG could be related to the different motor units in biceps brachii by using wavelet techniques coupled with principle component analysis. The results indicated that motor unit recruitment patterns can be indicated by the spectral properties of the EMG and MMG signals. EMG activity of 7 shoulder muscles was recorded with surface electrodes on 15 able-bodied participants over a range of manual wheelchair propulsion activities. Wavelet and principle component analysis was used to simultaneously decompose the signals into time and frequency domain. There are three main conclusions that can be drawn: 1) Uphill and faster speed (1.6m/s) propulsion required higher activity levels in the shoulder muscles and greater resultant joint force than did slow speed propulsion on the ergometer (0.9m/s), thus potentially\ud resulting in shoulder pathology. 2) Prolonged wheelchair propulsion and greater muscle activity may result in fatigue and play a factor in the development of shoulder pain and pathology over time. 3) The instructed semicircular pattern has a positive effect on shoulder muscle recruitment patterns. Further investigations need to focus on a systematic integrated data collection and analysis of kinematic, kinetic, and electromyography (EMG) data from people with spinal cord injuries

Kohti yläraaja-proteesien ohjausta pintaelektromyografialla

The loss of an upper limb is a life-altering accident which makes everyday life more difficult.A multifunctional prosthetic hand with an user-friendly control interface may significantlyimprove the life quality of amputees. However, many amputees do not use their prosthetichand regularly because of its low functionality, and low controllability. This situation callsfor the development of versatile prosthetic limbs that allow amputees to perform tasks thatare necessary for activities of daily living. The non-pattern based control scheme of the commercial state-of art prosthesis is rather poorand non-natural. Usually, a pair of muscles is used to control one degree of freedom. Apromising alternative to the conventional control methods is the pattern-recognition-basedcontrol that identifies different intended hand postures of the prosthesis by utilizing theinformation of the surface electromyography (sEMG) signals. Therefore, the control of theprosthesis becomes natural and easy. The objective of this thesis was to find the features that yield the highest classificationaccuracy in identifying 7 classes of hand postures in the context of Linear DiscriminantClassifier. The sEMG signals were measured on the skin surface of the forearm of the 8 ablebodiedsubjects. The following features were investigated: 16 time-domain features, twotime-serial-domain features, the Fast Fourier Transform (FFT), and the Discrete WaveletTransform (DWT). The second objective of this thesis was to study the effect of the samplingrate to the classification accuracy. A preprocessing technique, Independent ComponentAnalysis (ICA), was also shortly examined. The classification was based on the steady statesignal. The signal processing, features, and classification were implemented with Matlab. The results of this study suggest that DWT and FFT did not outperform the simple andcomputationally efficient time domain features in the classification accuracy. Thus, at least innoise free environment, the high classification accuracy (> 90 %) can be achieved with asmall number of simple TD features. A more reliable control may be achieved if the featuresare selected individually of a subset of the effective features. Using the sampling rate of 400Hz instead of commonly used 1 kHz may not only save the data processing time and thememory of the prosthesis controller but also slightly improve the classification accuracy.ICA was not found to improve the classification accuracy, which may be because themeasurement channels were placed relatively far from each other.Yläraaja-amputaatio vaikuttaa suuresti päivittäiseen elämään. Helposti ohjattavalla toiminnallisillaproteeseilla amputoitujen henkilöiden elämänlaatua voitaisiin parantaa merkittävästi.Suurin osa amputoiduista henkilöistä ei kuitenkaan käytä proteesiaan säännöllisesti proteesinvähäisten toimintojen ja vaikean ohjattavuuden vuoksi. Olisikin tärkeää kehittää helpostiohjattava ja riittävästi toimintoja sisältävä proteesi, joka mahdollistaisi päivittäisessäelämässä välttämättömien tehtävien suorittamisen. Markkinoilla olevat lihassähköiset yläraajaproteesit perustuvat yksinkertaiseen hahmontunnistustahyödyntämättömään ohjaukseen, jossa lihasparilla ohjataan yleensä yhtä proteesinvapausastetta. Lupaava vaihtoehto perinteisille ohjausmenetelmille on hahmontunnistukseenpohjautuva ohjaus. Se tunnistaa käyttäjän käden asennot käsivarren iholta mitatun lihassähkösignaalinsisältämän informaation avulla mahdollistaen helpon ja luonnollisen ohjauksen. Tämän diplomityön tavoitteena oli löytää piirteet, jolla seitsemän erilaista käden asentoa pystytäänluokittelemaan mahdollisimman tarkasti lineaarisella diskriminantti luokittelijalla.Lihassähkösignaalit mitattiin kahdeksan ei-amputoidun koehenkilön käsivarresta ihon pinnallekiinnitetyillä elektrodeilla. Työssä vertailtiin seuraavia piirteitä: 16 aika-alueen piirrettä,kaksi aikasarja-alueen piirrettä, nopea Fourier-muunnos (FFT), diskreetti Aallokemuunnos(DWT). Työn toinen tavoite oli tutkia näytteenottotaajuuden vaikutusta luokittelutarkkuuteen.Myös esiprosessointia riippumattomien komponenttien analyysillä tutkittiinlyhyesti. Luokittelu tehtiin staattisen lihassupistuksen aikana mitatun signaalin perusteella.Signaalin prosessointi, piirteet ja luokittelu toteutettiin Matlabilla. Tämän tutkimuksen tulokset osoittivat, etteivät diskreetti Aalloke-muunnos ja nopea Fouriermuunnosyllä laskennallisesti tehokkaampia aika-alueen piirteitä parempaan luokittelutarkkuuteen.Pienellä määrällä yksinkertaisia aika-alueen piirteitä voidaan saavuttaa hyvä luokittelutarkkuus(>90 %). Luokittelutarkkuutta voitaneen edelleen parantaa valitsemalla optimaalisetpiirteet yksilöllisesti pienestä joukosta hyviksi havaittuja piirteitä. Käyttämällä 400Hz:n näytteenottotaajuutta yleisesti käytetyn 1 kHz:n sijasta, voidaan sekä säästää prosessointiaikaaja proteesin prosessorin muistia että myös parantaa hieman luokittelutarkkuutta.Esiprosessointi riippumattomien komponenttien analyysillä ei parantanut luokittelutarkkuutta,mikä johtunee siitä, että mittauskanavat olivat suhteellisen kaukana toisistaan