Music-based biofeedback to reduce tibial shock in over-ground running : a proof-of-concept study

Methods to reduce impact in distance runners have been proposed based on real-time auditory feedback of tibial acceleration. These methods were developed using treadmill running. In this study, we extend these methods to a more natural environment with a proof-of-concept. We selected ten runners with high tibial shock. They used a music-based biofeedback system with headphones in a running session on an athletic track. The feedback consisted of music superimposed with noise coupled to tibial shock. The music was automatically synchronized to the running cadence. The level of noise could be reduced by reducing the momentary level of tibial shock, thereby providing a more pleasant listening experience. The running speed was controlled between the condition without biofeedback and the condition of biofeedback. The results show that tibial shock decreased by 27% or 2.96 g without guided instructions on gait modification in the biofeedback condition. The reduction in tibial shock did not result in a clear increase in the running cadence. The results indicate that a wearable biofeedback system aids in shock reduction during over-ground running. This paves the way to evaluate and retrain runners in over-ground running programs that target running with less impact through instantaneous auditory feedback on tibial shock

Tomographic neurofeedback : a new technique for the self-regulation of brain electrical activity

A major limitation of the current neurofeedback paradigm is the limited information provided by a single or a small number of electrodes placed on the scalp. A considerable improvement of the neurofeedback efficacy and specificity could be obtained feeding back brain activity of delimited structures. While traditional EEG information reflects the superposition of the electrical activity of a large number of neurons, by means of inverse solutions such as the Low-Resolution Electromagnetic Tomography (LORETA) spatially delimited brain activity can be evaluated in neocortical tissue. In this Dissertation we implement LORETA neurofeedback, we introduce a new feedback function ( 1 ) sensitive to dynamic change over time, and we clarify several issues related to the learning process observable with neurofeedback. The reported set of three experiments is the first attempt I am aware of to prove learning of brain current density activity. Three individuals were trained to improve brain activation (suppress low Alpha (8-10 Hz) and enhance low Beta (16 -20 Hz) current density) in the anterior cingulate gyros cognitive division (ACcd). Participants took part of six experimental sessions, each lasting approximately 30 minutes. Randomization-Permutation ANCOVA tests were conducted on recordings of the neurofeedback training. In addition a randomized trial was performed at the end of the treatment. During eight two-minutes periods (trials) participants were asked to try to obtain as many rewards as they could ( 4 1 trials) or as few rewards as they could ( 4 0 trials). The order of trials was decided at random. The hypothesis under testing was that participants acquired volitional control over their brain activity so to be able to obtain more rewards during the plus condition as compared to the minus condition. We found evidence of volitional control for two subjects (p=0.043 and p=O.l) and no evidence of volitional control for one of them (p=0.27 1). The combination of the three p-values provided an overall probability value for this experiment of 0.012 with the additive method and 0.035 with the multiplicative method. These results strongly support the hypothesis of volitional control across the experimental group. Trends of the Beta/ Alpha power ratio in the ACcd were in the expected direction for all the three subjects, however the combined p-values did not reach significance. With as few as six training sessions, typically insufficient to produce any form of learning with scalp neurofeedback, the experiment showed overall signs of volitional control of the electrical activity of the ACcd. Possible applications of the technique are important and include the treatment of epileptic foci, the treatment of specific brain regions damaged as a consequence of traumatic brain injury, and in general of any specific cortical electrical activity

Acoustic measurement of overall voice quality in sustained vowels and continuous speech

Measurement of dysphonia severity involves auditory-perceptual evaluations and acoustic analyses of sound waves. Meta-analysis of proportional associations between these two methods showed that many popular perturbation metrics and noise-to-harmonics and others ratios do not yield reasonable results. However, this meta-analysis demonstrated that the validity of specific autocorrelation- and cepstrum-based measures was much more convincing, and appointed ‘smoothed cepstral peak prominence’ as the most promising metric of dysphonia severity. Original research confirmed this inferiority of perturbation measures and superiority of cepstral indices in dysphonia measurement of laryngeal-vocal and tracheoesophageal voice samples. However, to be truly representative for daily voice use patterns, measurement of overall voice quality is ideally founded on the analysis of sustained vowels ánd continuous speech. A customized method for including both sample types and calculating the multivariate Acoustic Voice Quality Index (i.e., AVQI), was constructed for this purpose. Original study of the AVQI revealed acceptable results in terms of initial concurrent validity, diagnostic precision, internal and external cross-validity and responsiveness to change. It thus was concluded that the AVQI can track changes in dysphonia severity across the voice therapy process. There are many freely and commercially available computer programs and systems for acoustic metrics of dysphonia severity. We investigated agreements and differences between two commonly available programs (i.e., Praat and Multi-Dimensional Voice Program) and systems. The results indicated that clinicians better not compare frequency perturbation data across systems and programs and amplitude perturbation data across systems. Finally, acoustic information can also be utilized as a biofeedback modality during voice exercises. Based on a systematic literature review, it was cautiously concluded that acoustic biofeedback can be a valuable tool in the treatment of phonatory disorders. When applied with caution, acoustic algorithms (particularly cepstrum-based measures and AVQI) have merited a special role in assessment and/or treatment of dysphonia severity

Physiological Self Regulation with Biofeedback Games

Mental stress is a global epidemic that can have serious health consequences including cardiovascular diseases and diabetes. Several techniques are available to teach stress self-regulation skills including therapy, meditation, deep breathing, and biofeedback. While effective, these methods suffer from high drop-outs due to the monotonic nature of the exercises and are generally practiced in quiet relaxed environment, which may not transfer to real-world scenarios. To address these issues, this dissertation presents a novel intervention for stress training using games and wearable sensors. The approach consists of monitoring the user’s physiological signals during gameplay, mapping them into estimates of stress levels, and adapting the game in a way that promotes states of low arousal. This approach offers two key advantages. First, it allows users to focus on the gameplay rather than on monitoring their physiological signals, which makes the training far more engaging. More importantly, it teaches users to self-regulate their stress response, while performing a task designed to increase arousal. Within this broad framework, this dissertation studies three specific problems. First, the dissertation evaluates three physiological signals (breathing rate, heart rate variability, and electrodermal activity) that span across the dimensions of degrees of selectivity in measuring arousal and voluntary control in their effectiveness in lowering arousal. This will identify the signal appropriate for game based stress training and the associated bio-signal processing techniques for real-time arousal estimation. Second, this dissertation investigates different methods of biofeedback presentation e.g. visual feedback and game adaptation during gameplay. Selection of appropriate biofeedback mechanism is critical since it provides the necessary information to improve the perception of visceral states (e.g. stress) to the user. Furthermore, these modalities facilitate skill acquisition in distinct ways (i.e., top-down and bottom-up learning) and influence retention of skills. Third, this dissertation studies reinforcement scheduling in a game and its effect on skill learning and retention. A reinforcement schedule determines which occurrences of the target response are reinforced. This study focuses on continuous and partial reinforcement schedules in GBF and their effect on resistance to extinction (i.e. ability to retain learned skills) after the biofeedback is removed. The main contribution of this dissertation is in demonstrating that stress self-regulation training can be embedded in videogames and help individuals develop more adaptive responses to reduce physiological stress encountered both at home and work

Gait sonification for rehabilitation: adjusting gait patterns by acoustic transformation of kinematic data

To enhance motor learning in both sport and rehabilitation, auditory feedback has emerged as an effective tool. Since it requires less attention than visual feedback and hardly affects the visually dominated orientation in space, it can be used safely and effectively in natural locomotion such as walking. One method for generating acoustic movement feedback is the direct mapping of kinematic data to sound (movement sonification). Using this method in orthopedic gait rehabilitation could make an important contribution to the prevention of falls and secondary diseases. This would not only reduce the individual suffering of the patients, but also medical treatment costs. To determine the possible applications of movement sonification in gait rehabilitation in the context of this work, a new gait sonification method based on inertial sensor technology was developed. Against the background of current scientific findings on sensorimotor function, feedback methods, and gait analysis, three studies published in scientific journals are presented in this thesis: The first study shows the applicability and acceptance of the feedback method in patients undergoing inpatient rehabilitation after unilateral total hip arthroplasty. In addition, the direct effect of gait sonification during ten gait training sessions on the patients’ gait pattern was revealed. In the second study, the immediate follow-up effect of gait sonification on the kinematics of the same patient group is examined at four measurement points after gait training. In this context, a significant influence of sonification on the gait pattern of the patients was shown, which, however, did not meet the previously expected effects. In view of this finding, the effect of the specific sound parameter loudness of gait sonification on the gait of healthy persons was analyzed in a third study. Thus, an impact of asymmetric loudness of gait sonification on the ground contact time could be detected. Considering this cause-effect relationship can be a component in improving gait sonfication in rehabilitation. Overall, the feasibility and effectiveness of movement sonification in gait rehabilitation of patients after unilateral hip arthroplasty becomes evident. The findings thus illustrate the potential of the method to efficiently support orthopedic gait rehabilitation in the future. On the basis of the results presented, this potential can be exploited in particular by an adequate mapping of movement to sound, a systematic modification of selected sound parameters, and a target-group-specific selection of the gait sonification mode. In addition to a detailed investigation of the three factors mentioned above, an optimization and refinement of gait analysis in patients after arthroplasty using inertial sensor technology will be beneficial in the future.Akustisches Feedback kann wirkungsvoll eingesetzt werden, um das Bewegungslernen sowohl im Sport als auch in der Rehabilitation zu erleichtern. Da es weniger Aufmerksamkeit als visuelles Feedback erfordert und die visuell dominierte Orientierung im Raum kaum beeinträchtigt, kann es während einer natürlichen Fortbewegung wie dem Gehen sicher und effektiv genutzt werden. Eine Methode zur Generierung akustischen Bewegungsfeedbacks ist die direkte Abbildung kinematischer Daten auf Sound (Bewegungssonifikation). Ein Einsatz dieser Methode in der orthopädischen Gangrehabilitation könnte einen wichtigen Beitrag zur Prävention von Stürzen und Folgeerkrankungen leisten. Neben dem individuellen Leid der Patienten ließen sich so auch medizinische Behandlungskosten erheblich reduzieren. Um im Rahmen dieser Arbeit die Einsatzmöglichkeiten der Bewegungssonifikation in der Gangrehabilitation zu bestimmen, wurde eine neue Gangsonifikationsmethodik auf Basis von Inertialsensorik entwickelt. Zu der entwickelten Methodik werden, vor dem Hintergrund aktueller wissenschaftlicher Erkenntnisse zur Sensomotorik, zu Feedbackmethoden und zur Ganganalyse, in dieser Thesis drei in Fachzeitschriften publizierte Studien vorgestellt. Die erste Studie beschreibt die Anwendbarkeit und Akzeptanz der Feedbackmethode bei Patienten in stationärer Rehabilitation nach unilateraler Hüftendoprothetik. Darüber hinaus wird der direkte Effekt der Gangsonifikation während eines zehnmaligen Gangtrainings auf das Gangmuster der Patienten deutlich. In der zweiten Studie wird der unmittelbare Nacheffekt der Gangsonifikation auf die Kinematik der gleichen Patientengruppe zu vier Messzeitpunkten nach dem Gangtraining untersucht. In diesem Zusammenhang zeigte sich ein signifikanter Einfluss der Sonifikation auf das Gangbild der Patienten, der allerdings nicht den zuvor erwarteten Effekten entsprach. Aufgrund dieses Ergebnisses wurde in einer dritten Studie die Wirkung des spezifischen Klangparameters Lautstärke der Gangsonifikation auf das Gangbild von gesunden Personen analysiert. Dabei konnte ein Einfluss von asymmetrischer Lautstärke der Gangsonifikation auf die Bodenkontaktzeit nachgewiesen werden. Die Berücksichtigung dieses Ursache-Wirkungs-Zusammenhangs kann einen Baustein bei der Verbesserung der Gangsonifikation in der Rehabilitation darstellen. Insgesamt wird die Anwendbarkeit und Wirksamkeit von Bewegungssonifikation in der Gangrehabilitation bei Patienten nach unilateraler Hüftendoprothetik evident. Die gewonnenen Erkenntnisse verdeutlichen das Potential der Methode, die orthopädische Gangrehabilitation zukünftig effizient zu unterstützen. Ausschöpfen lässt sich dieses Potential auf Grundlage der vorgestellten Ergebnisse insbesondere anhand einer adäquaten Zuordnung von Bewegung zu Sound, einer systematischen Modifikation ausgewählter Soundparameter sowie einer zielgruppenspezifischen Wahl des Modus der Sonifikation. Neben einer differenzierten Untersuchung der genannten Faktoren, erscheint zukünftig eine Optimierung und Verfeinerung der Ganganalyse bei Patienten nach Endoprothetik unter Einsatz von Inertialsensorik notwendig

Optimization of the position of single-lead wireless sensor with low electrodes separation distance for ECG-derived respiration

A classical method for estimation of respiratory information from electrocardiogram (ECG), called ECG - derived respiration (EDR), is using flexible electrodes located at standard electrocardiography positions. This work introduces an alternative approach suitable for miniaturized sensors with low inter-electrode separation and electrodes fixed to the sensor encapsulation. Application of amplitude EDR algorithm on single-lead wireless sensor system with optimized electrode positions shows results comparable with standard robust systems. The modified method can be applied in daily physiological monitoring, in sleep studies or implemented in smart clothes when standard respiration techniques are not suitable

Exploring machine learning, real-time bio-feedback, and inertial sensor accuracy for the prevention of running-related injuries

Recreational running is popular, however, incident rates of running related injuries (RRIs) are very high. Predisposition to injury can be assessed through expensive, laboratory-based biomechanical screening. Wearable wireless inertial sensors offer a potential solution, but accurate orientation data are required. This thesis examined the prevention of RRIs, by aiming to improve sensor accuracy, and investigate applications of biofeedback and machine learning. This thesis explored improving (magnetometer-free) orientation accuracy during running, through examination of (i) Z-axis de-drifting, (ii) data-loss (iii) and modifications to the Madgwick filter. Despite some accuracy improvements (i, iii), overall errors were unsuitable for running based applications. Impact loading is associated with RRIs, with thigh angle (quasi-measure of knee-flexion) potentially important in load attenuation. Loading can be altered directly (loading-based biofeedback) or indirectly (technique-based biofeedback), these two types of biofeedback were compared. A mobile phone application was developed providing audio biofeedback to reduce impact accelerations and encourage a ‘softer’ running technique. Both types of feedback reduced loading at the tibia and sacrum, however, tibia loading reduced better with impact accelerations biofeedback, and sacrum loading with thigh angle biofeedback. It would be beneficial to identify runners who may be predisposed to injury. Seven supervised machine learning models were developed to identify runners who may be likely to sustain RRIs, using inertial, kinematic and clinical data collected on 150 prospectively tracked runners. These models resulted in weak predictive accuracy (0.58-0.61 AUC). As we cannot identify runners predisposed to injury, all runners must be recommended for injury prevention interventions. Orientation accuracy was found to be sufficient for relative measures of running technique in the biofeedback app. Future work could investigate biofeedback app use in relation to reduction of RRIs. Additionally, running injury prediction could be examined further with respect to extracting different features (continuous measures) or predicting specific injuries

Master of Science

thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on a cell phone, to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile devices. As the availability of powerful mobile computing devices expands, the road is paved for applications in previously limited environments. Rehabilitative devices are emerging that embrace these mobile advances. Research has explored the use of smartphones in rehabilitation as a means to process data and provide feedback in conjunction with established rehabilitative methods. Smartphones, combined with sensor embedded insoles, provide a powerful tool for the clinician in gathering data and may act as a standalone training technique. This thesis presents continuing research of a sensor integrated insole system that provides real-time feedback through a mobile platform, the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction (ARTISTIC). The system interfaces a wireless instrumented insole with an Android smartphone application to receive gait data and provide sensory feedback to modify gait patterns. Revisions to the system hardware, software, and feedback modes brought about the introduction of the ARTISTIC 2.0. The number of sensors in the insole was increased from two to 10. The microprocessor and a vibrotactile motor were embedded in the insole and the communications box was reduced in size and weight by more than 50%. Stance time iv measurements were validated against force plate equipment and found to be within 13.5 ± 3.3% error of force plate time measurements. Human subjects were tested using each of the feedback modes to alter gait symmetry. Results from the testing showed that more than one mode of feedback caused a statistically significant change in gait symmetry ratios (p < 0.05). Preference of feedback modes varied among subjects, with the majority agreeing that several feedback modes made a difference in their gait. Further improvements will prepare the ARTISTIC 2.0 for testing in a home environment for extended periods of time and improve data capture techniques, such as including a database in the smartphone application