Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Designing Auditory Feedback from Wearable Weightlifting Devices

While wearable devices for fitness have gained broad popularity, most are focused on tracking general activity types rather than correcting exercise forms, which is extremely important for weightlifters. We interviewed 7 frequent gym-goers about their opinions and expectations for feedback from wearable devices for weightlifting. We describe their desired feedback, and how their expectations and concerns could be balanced in future wearable fitness technologies

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Supervised machine learning applied to wearable sensor data can accurately classify functional fitness exercises within a continuous workout

Observing, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter- and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbors (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8–18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training

Intelligent Sensing Techniques for Desk Workplace Ergonomics

Wearables mit integrierten inertialen Messeinheiten ermöglichen neue Sensoranwendungen, die über längere Zeiträume hinweg messen. Die Arbeitsergonomie ist ein besonderer Bereich, in dem Wearables und intelligente Sensorik die Gesundheit und das Wohlbefinden vieler Menschen verbessern können. Personen in Büroumgebungen sind aufgrund anhaltender Fehlhaltungen besonders anfällig für Muskel-Skelett-Erkrankungen. Haltungsbedingte Probleme sind dadurch inzwischen der dritthäufigste gemeldete Risikofaktor für die Gesundheit am Arbeitsplatz. Das Ziel dieser Arbeit ist es, die Anwendung von Edge-AI-Technologien für eine kontinuierliche Bewertung ergonomischer Risikofaktoren am Schreibtischarbeitsplatz zu untersuchen. Der erstellte Ansatz bewertet die ergonomische Risikosituation eines Nutzers auf Basis der Körperhaltung und der Arbeitsplatzbedingungen und leitet daraus Verbesserungsvorschläge ab. Relevante Bewegungen werden kontinuierlich von einem Sensornetzwerk mit Beschleunigungssensoren und Gyroskopen erfasst, die an einem tragbaren Brillenrahmen sowie an einer Armlehne eines Bürostuhls angebracht sind. Diese Arbeit schlägt ein neuartiges domänenspezifisches maschinelles Lernverfahren und Zustandsübergangsmodell für die langfristige Ableitung von Körperhaltungen durch die Klassifizierung und Aggregation von Ereignissen vor, die in den Sensorzeitreihen auftreten. Zusätzlich wird ein Manifold von Bewegungen erstellt, der Schlussfolgerungen und Empfehlungen für ergonomisch relevante Körperbewegungen ermöglicht. Schließlich demonstriert eine Software-Implementierung die Techniken erfolgreich in der Praxis

Challenges in context-aware mobile language learning: the MASELTOV approach

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment

Contributions to physical exercises monitoring with inertial measurement units

Resumen: La monitorización de movimientos trata de obtener información sobre su ejecución, siendo esencial en múltiples aplicaciones, como el seguimiento de terapias físicas. La monitorización tiene un doble objetivo esencial para lograr los beneficios de dichas terapias: asegurar la corrección en la ejecución de movimientos y mejorar la adherencia a los programas prescritos. Para lograr esta monitorización de forma remota y poco intrusiva, se necesitan recursos tecnológicos. Este trabajo se centra en las soluciones basadas en sensores inerciales. Esta tesis estudia los algoritmos de la literatura para el análisis de movimientos con sensores inerciales, determinando un parámetro anatómico requerido en diversas propuestas: la posición de las articulaciones respecto de los sensores, así como longitud de los segmentos anatómicos. En este trabajo se introducen dos algoritmos de calibración anatómica. El primero, basado en mínimos cuadrados, determina el punto o el eje medios de aceleración nula presente en las articulaciones fijas. El algoritmo está adaptado a los movimientos lentos dados en los miembros inferiores para estabilizar las articulaciones. El segundo, adaptado a la variación de la posición relativa del punto de aceleración nula respecto de los sensores a causa del característico tejido blando asociado al cuerpo humano, emplea las medidas inerciales como entradas en un filtro de Kalman extendido. Por otro lado, esta tesis aborda la falta de datos comunes para la evaluación y comparación de los algoritmos. Para ello, se diseña y crea una base de datos centrada en movimientos habituales en rutinas físicas, que se encuentra publicada en Zenodo. Esta base de datos contiene movimientos de calibración articular y de ejercicios de miembros inferiores y superiores ejecutados de forma correcta e incorrecta por 30 voluntarios de ambos sexos con un amplio rango de edades, grabados con cuatro sensores inerciales y un sistema de referencia óptico de alta precisión. Además, las grabaciones se encuentran etiquetadas acorde al tipo de ejercicio realizado y su evaluación. Finalmente, se estudia un segundo enfoque de monitorización de rutinas físicas, cuyo objetivo es reconocer y evaluar simultáneamente los ejercicios ejecutados, retos comúnmente estudiados individualmente. Se proponen tres sistemas que emplean las medidas de cuatro sensores inerciales y difieren en el nivel de detalle en las salidas del sistema. Para realizar las clasificaciones propuestas, se evalúan seis algoritmos de machine learning determinando el más adecuado.This thesis is framed in the field of remote motion monitoring, which aims to obtain information about the execution of movements. This information is essential in many applications, including the clinical ones, to measure the evolution of patients, to assess possible pathologies, such as motor or cognitive ones, and to follow up physical therapies. The monitoring of physical therapies has twofold purpose: to ensure the correct execution of movements and to improve adherence to the programs. Both purposes are essential to achieve the benefits associated with physical therapies. To accomplish this monitoring in a remote and non-intrusive way, technological resources such as the well-known inertial sensors are needed, which are commonly integrated into the so-called wearables. This work focuses on inertial-based solutions for monitoring physical therapy routines. However, the results of this work are not exclusive of this field, being able to be applied in other fields that require a motion monitoring. This work is intended to meet the needs of the monitoring systems found in the literature. In the review of previous proposals for remote monitoring of rehabilitation routines, we found two different main approaches. The first one is based on the analysis of movements, which estimates kinematic parameters, and the second one focuses on the qualitative characterization of the movements. From this differentiation, we identify and contribute to the limitations of each approach. With regard to the motion analysis for the estimation of kinematic parameters, we found an anatomical parameter required in various methods proposed in the literature. This parameter consists in the position of the joints with respect to the sensors, and sometimes these methods also require the length of the anatomical segments. The determination of these internal parameters is complex and is usually performed in controlled environments with optical systems or through palpation of anatomical landmarks by trained personnel. There is a lack of algorithms that determine these anatomical parameters using inertial sensors. This work introduces an algorithm for this anatomical calibration, which is based on the determination of the point of zero acceleration present in fixed joints. We use one inertial sensor per joint in order to simplify the complexity of algorithms versus using xv xvi ABSTRACT more than one. Since the relative position of this point may vary due to soft tissue movements or joint motion, the mean null acceleration point for the calibration motion is estimated by least squares. This algorithm is adapted to slow movements occurring in the lower-limbs to meet the required joint stabilization. Moreover, it can be applied to both joint centers and axes, although the latter is more complex to determine. Since we are dealing with the calibration of a system as complex as the human body, we evaluate different movements and their relation to the accuracy of the proposed system. This thesis also proposes a second, more versatile calibration method, which is adapted to the characteristic soft tissue associated with the human body. This method is based on the measurements of one inertial sensors used as inputs of an extended Kalman filter. We test the proposal both in synthetic data and in the real scenario of hip center of rotation determination. In simulations it provides an accuracy of 3% and in the real scenario, where the reference is obtained with a high precision optical system, the accuracy is 10 %. In this way, we propose a novel algorithm that localizes the joints adaptively to the motion of the tissues. In addition, this work addresses another limitation of motion analysis which is the lack of common datasets for the evaluation of algorithms and for the development of new proposals of motion monitoring methods. For this purpose, we design and create a public database focused on common movements in rehabilitation routines. Its design takes into account the joint calibration that is usually considered for the monitoring of joint parameters, performing functional movements for it. We monitor lower and upper limb exercises correctly and incorrectly performed by 30 volunteers of both sexes and a wide range of ages. One of the main objectives to be fulfilled by this database is the validation of algorithms based on inertial systems. Thus, it is recorded by using four inertial systems placed on different body limbs and including a highly accurate reference system based on infrared cameras. In addition, the recorded movements are labeled according to their characterization, which is based on the type of exercise performed and their quality. We provide a total of 7 076 files of inertial kinematic data with a high-precision reference, characterized with respect to the kind of performed motion and their correctness in performance, together with a function for automatic processing. Finally, we focus on the analysis of the second approach of monitoring physical routines, whose objective is to obtain qualitative information of their execution. This work contributes to the characterization of movements including their recognition and evaluation, which are usually studied separately. We propose three classification systems which use four inertial sensors. The proposals differ in the distribution of data and, therefore, the level of detail in the system outputs. We evaluate six machine learning techniques for the proposed classification systems in order to determine the most suitable for each of them: Support Vector Machines, Decision Trees, Random Forest, xvii K Nearest Neighbors, Extreme Learning Machines and Multi-Layer Perceptron. The proposals result in accuracy, F1-value, precision and sensitivity above the 88 %. Furthermore, we achieve a system with an accuracy of 95% in the complete qualitative characterization of the motions, which recognizes the performed motion and evaluates the correctness of its performance. It is worth highlighting that the highest metrics are always obtained with Support Vector Machines, among all the methods evaluated. The proposed classifier that provides the highest metrics is the one divided into two stages, that first recognizes the exercises and then evaluates them, compared with the other proposals that perform both tasks in one single-stage classification. From our work, it can be concluded that inertial systems are appropriate for remote physical exercise monitoring. On the one hand, they are suitable for the calibration of human joints necessary for various methods of motion analysis using one inertial sensor per joint. These sensors allow to obtain the estimation of an average joint location as well as the average length of anatomical segments. Also, joint centers can be located in scenarios where joint-related sensor movements occur, associated with soft tissue movement. On the other hand, a complete characterization of the physical exercises performed can be achieved with four inertial sensors and the appropriate algorithms. In this way, anatomical information can be obtained, as well as quantitative and qualitative information on the execution of physical therapies through the use of inertial sensors

Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation