Blind Source Separation Based Classification Scheme for Myoelectric Prosthesis Hand

For over three decades, researchers have been working on using surface electromyography (sEMG) as a means for amputees to use remaining muscles to control prosthetic limbs (Baker, Scheme, Englehart, Hutcinson, & Greger, 2010; Hamdi, Dweiri, Al-Abdallat, & Haneya, 2010; Kiguchi, Tanaka, & Fukuda, 2004). Most research in this domain has focused on using the muscles of the upper arms and shoulders to control the gross orientation and grasp of a low-degree-of-freedom prosthetic device for manipulating objects (Jacobsen & Jerard, 1974). Each measured upper arm muscle is typically mapped directly to one degree of freedom of the prosthetic. For example, tricep contraction could be used for rotation while bicep flexion might close or open the prosthetic. More recently, researchers have begun to look at the potential of using the forearm muscles in hand amputees to control a multi-fingered prosthetic hand. While we know of no fully functional hand prosthetic, this is clearly a promising new area of EMG research. One of the challenges for creating hand prosthetics is that there is not a trivial mapping of individual muscles to finger movements. Instead, many of the same muscles are used for several different fingers (Schieber, 1995)

Neuromorphic decoding of spinal motor neuron behaviour during natural hand movements for a new generation of wearable neural interfaces

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ±0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN

Human Breathing Classification Using Electromyography Signal with Features Based on Mel-Frequency Cepstral Coefficients

Typical method on assessing the human breathing characteristics is based on measurements of breathing air parameters. Another possible method for human breathing assessment is through the analysis of respiratory muscles electromyography (EMG) signal. The EMG signal from different breathing task will be analyzed in order to determine the characteristics of the EMG signal pattern. Thus, feature extraction need to be done on the EMG signals. This paper  will look into the use of Mel-Frequency Cepstral Coefficients (MFCC) in providing the features for EMG signal. Analysis is done using different data analysis frame sizes. EMG signal classification is done using K-Nearest Neighbour. Results shows that MFCC is a good feature extraction method for this purpose with classification accuracy exceeds more than 90% for data analysis frame size of 2000 ms, 4000 ms, 5000 ms and 10000 ms

Classification of Myopotentials of Hand's Motion to Control Applications

Import 23/08/2017V této diplomové práci je realizován systém pro klasifikaci myopotenciálů gest ruky. Prvním cílem bylo vytvořit hardware, který by byl schopen přenést nezarušený a správně zesílený signál myopotenciálů svalů ke zpracování do PC. Druhým cílem bylo naprogramovat algoritmus, který myopotenciály klasifikuje do určených gest ruky. Kombinací filtrů 2. řádu a správného zesílení byl vytvořen hardwarový prototyp obsahující čtyři měřící kanály pro snímání myopotenciálů. Z důvodu použití aktivních elektrod je uživatel galvanicky oddělen od zdroje. Pro digitalizaci a přenos dat byl vybrán mikrokontrolér Arduino Nano a naprogramován dle vytvořeného komunikačního protokolu. Programování počítačové aplikace je realizováno v jazyce C#. Zpracování signálu a grafické zobrazení měřeného signálu probíhá v reálném čase. Dle algoritmu adaptivní segmentace je zjišťována hranice provedeného gesta. Pomocí navržených fuzzy množin a systému váhování je určeno jedno z pěti (nebo žádné) gest ruky, které bylo provedeno.Realization of the system for classification of hand’s gestures is described in this master’s thesis. The first goal was to create hardware that would be able to measure signal of myopotentials for computer analysis without external noise and with right amplification. The second goal was to program an algorithm which could classify specific gestures of hand. Hardware prototype of four measuring channels was created by combination of 2nd order filters and right amount amplification. The user is isolated from the power source using galvanic isolation because of usage of active electrodes. For digitizing the data, the Arduino Nano microcontroller was selected and programed using defined communication protocol. The computer software is programed in C# programming language. Signal processing and drawing to user interface is in real time. The one of five possible gestures that user made is chosen using fuzzy logic and designed system of scaling.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN

Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance

Proficiency-aware systems

In an increasingly digital world, technological developments such as data-driven algorithms and context-aware applications create opportunities for novel human-computer interaction (HCI). We argue that these systems have the latent potential to stimulate users and encourage personal growth. However, users increasingly rely on the intelligence of interactive systems. Thus, it remains a challenge to design for proficiency awareness, essentially demanding increased user attention whilst preserving user engagement. Designing and implementing systems that allow users to become aware of their own proficiency and encourage them to recognize learning benefits is the primary goal of this research. In this thesis, we introduce the concept of proficiency-aware systems as one solution. In our definition, proficiency-aware systems use estimates of the user's proficiency to tailor the interaction in a domain and facilitate a reflective understanding for this proficiency. We envision that proficiency-aware systems leverage collected data for learning benefit. Here, we see self-reflection as a key for users to become aware of necessary efforts to advance their proficiency. A key challenge for proficiency-aware systems is the fact that users often have a different self-perception of their proficiency. The benefits of personal growth and advancing one's repertoire might not necessarily be apparent to users, alienating them, and possibly leading to abandoning the system. To tackle this challenge, this work does not rely on learning strategies but rather focuses on the capabilities of interactive systems to provide users with the necessary means to reflect on their proficiency, such as showing calculated text difficulty to a newspaper editor or visualizing muscle activity to a passionate sportsperson. We first elaborate on how proficiency can be detected and quantified in the context of interactive systems using physiological sensing technologies. Through developing interaction scenarios, we demonstrate the feasibility of gaze- and electromyography-based proficiency-aware systems by utilizing machine learning algorithms that can estimate users' proficiency levels for stationary vision-dominant tasks (reading, information intake) and dynamic manual tasks (playing instruments, fitness exercises). Secondly, we show how to facilitate proficiency awareness for users, including design challenges on when and how to communicate proficiency. We complement this second part by highlighting the necessity of toolkits for sensing modalities to enable the implementation of proficiency-aware systems for a wide audience. In this thesis, we contribute a definition of proficiency-aware systems, which we illustrate by designing and implementing interactive systems. We derive technical requirements for real-time, objective proficiency assessment and identify design qualities of communicating proficiency through user reflection. We summarize our findings in a set of design and engineering guidelines for proficiency awareness in interactive systems, highlighting that proficiency feedback makes performance interpretable for the user.In einer zunehmend digitalen Welt schaffen technologische Entwicklungen - wie datengesteuerte Algorithmen und kontextabhängige Anwendungen - neuartige Interaktionsmöglichkeiten mit digitalen Geräten. Jedoch verlassen sich Nutzer oftmals auf die Intelligenz dieser Systeme, ohne dabei selbst auf eine persönliche Weiterentwicklung hinzuwirken. Wird ein solches Vorgehen angestrebt, verlangt dies seitens der Anwender eine erhöhte Aufmerksamkeit. Es ist daher herausfordernd, ein entsprechendes Design für Kompetenzbewusstsein (Proficiency Awareness) zu etablieren. Das primäre Ziel dieser Arbeit ist es, eine Methodik für das Design und die Implementierung von interaktiven Systemen aufzustellen, die Nutzer dabei unterstützen über ihre eigene Kompetenz zu reflektieren, um dadurch Lerneffekte implizit wahrnehmen können. Diese Arbeit stellt ein Konzept für fähigkeitsbewusste Systeme (proficiency-aware systems) vor, welche die Fähigkeiten von Nutzern abschätzen, die Interaktion entsprechend anpassen sowie das Bewusstsein der Nutzer über deren Fähigkeiten fördern. Hierzu sollten die Systeme gesammelte Daten von Nutzern einsetzen, um Lerneffekte sichtbar zu machen. Die Möglichkeit der Anwender zur Selbstreflexion ist hierbei als entscheidend anzusehen, um als Motivation zur Verbesserung der eigenen Fähigkeiten zu dienen. Eine zentrale Herausforderung solcher Systeme ist die Tatsache, dass Nutzer - im Vergleich zur Abschätzung des Systems - oft eine divergierende Selbstwahrnehmung ihrer Kompetenz haben. Im ersten Moment sind daher die Vorteile einer persönlichen Weiterentwicklung nicht unbedingt ersichtlich. Daher baut diese Forschungsarbeit nicht darauf auf, Nutzer über vorgegebene Lernstrategien zu unterrichten, sondern sie bedient sich der Möglichkeiten interaktiver Systeme, die Anwendern die notwendigen Hilfsmittel zur Verfügung stellen, damit diese selbst über ihre Fähigkeiten reflektieren können. Einem Zeitungseditor könnte beispielsweise die aktuelle Textschwierigkeit angezeigt werden, während einem passionierten Sportler dessen Muskelaktivität veranschaulicht wird. Zunächst wird herausgearbeitet, wie sich die Fähigkeiten der Nutzer mittels physiologischer Sensortechnologien erkennen und quantifizieren lassen. Die Evaluation von Interaktionsszenarien demonstriert die Umsetzbarkeit fähigkeitsbewusster Systeme, basierend auf der Analyse von Blickbewegungen und Muskelaktivität. Hierbei kommen Algorithmen des maschinellen Lernens zum Einsatz, die das Leistungsniveau der Anwender für verschiedene Tätigkeiten berechnen. Im Besonderen analysieren wir stationäre Aktivitäten, die hauptsächlich den Sehsinn ansprechen (Lesen, Aufnahme von Informationen), sowie dynamische Betätigungen, die die Motorik der Nutzer fordern (Spielen von Instrumenten, Fitnessübungen). Der zweite Teil zeigt auf, wie Systeme das Bewusstsein der Anwender für deren eigene Fähigkeiten fördern können, einschließlich der Designherausforderungen , wann und wie das System erkannte Fähigkeiten kommunizieren sollte. Abschließend wird die Notwendigkeit von Toolkits für Sensortechnologien hervorgehoben, um die Implementierung derartiger Systeme für ein breites Publikum zu ermöglichen. Die Forschungsarbeit beinhaltet eine Definition für fähigkeitsbewusste Systeme und veranschaulicht dieses Konzept durch den Entwurf und die Implementierung interaktiver Systeme. Ferner werden technische Anforderungen objektiver Echtzeitabschätzung von Nutzerfähigkeiten erforscht und Designqualitäten für die Kommunikation dieser Abschätzungen mittels Selbstreflexion identifiziert. Zusammengefasst sind die Erkenntnisse in einer Reihe von Design- und Entwicklungsrichtlinien für derartige Systeme. Insbesondere die Kommunikation, der vom System erkannten Kompetenz, hilft Anwendern, die eigene Leistung zu interpretieren