The use of inertial measurement units for the determination of gait spatio-temporal parameters

The aim of this work was to develop a methodology whereby inertial measurement units (IMUs) could be used to obtain accurate and objective gait parameters within typical developed adults (TDA) and Parkinson’s disease (PD). The thesis comprised four studies, the first establishing the validity of the IMU method when measuring the vertical centre of mass (CoM) acceleration, velocity and position versus an optical motion capture system (OMCS) in TDA. The second study addressed the validity of the IMU and inverted pendulum model measurements within PD and also explored the inter-rater reliability of the measurement. In the third study the optimisation of the inverted pendulum model driven by IMU data was explored when comparing to standardised clinical tests within TDA and PD, and the fourth explored a novel phase plot analysis applied to CoM movement to explore gait in more detail. The validity study showed no significant difference for vertical acceleration and position between IMU and OMCS measurements within TDA. Vertical velocity however did show a significant difference, but the error was still less than 2.5%. ICCs for all three parameters ranged from 0.782 to 0.952, indicating an adequate test-retest reliability. Within PD there was no significant difference found for vertical CoM acceleration, velocity and position. ICCs for all three parameters ranged from 0.77 to 0.982. In addition, the reliability calculations found no difference for step time, stride length and walking speed for people with PD. Inter-rater reliability was found not to be different for the same parameters. The optimisation of the correction factor when using the inverted pendulum model showed no significant difference between TDA and PD. Furthermore the correction factor was found not to be related to walking speed. The fourth and final study found that phase plot analysis of variability could be performed on CoM vertical excursion. TDA and PD were shown to have, on average, different characteristics. This thesis demonstrated that CoM motion can be objectively measured within a clinical setting in people with PD by utilizing IMUs. Furthermore, in depth gait variability analysis can be performed by utilizing a phase plot method

Measuring sociogenic, behavioral, and environmental impacts on circadian and rest-activity rhythms in healthy and pathological populations using actigraphy

Few biological systems are as ubiquitous as the circadian rhythm, a distributed yet inter-connected “system of systems” that coordinates the timing of physiological processes via a self-regulating, flexible network present at every level of biological organization, from cells to cities. Its functional role as the interface between time-dependent internal processes and external environmental cues exposes the circadian rhythm to disruption if these drift out of synchrony. This is especially common in industrialized human societies, where the abun-dance of resources – in combination with the fact that anthropogenic calendars have largely supplanted the sun as the primary determinant of our daily cycles of rest, activity, and sleep – disrupts the circadian rhythm’s ability to synchronize biological processes with each other and the geophysical solar day. Humans are now beholden to two increasingly disconnected clocks, and the ever-accelerating curve of human progress suggests our biological and so-cial times will only grow more disconnected. Longitudinal “out-of-clinic” monitoring is an ecologically valid alternative to well-controlled laboratory studies that can provide insight into how human circadian and behav-ioral rhythms exist in day-to-day life, and so has great potential to provide contextual data for translating chronobiological science into clinical intervention. However, methodological diversity, inconsistent terminology, insufficient reporting, and the sheer number of potential factors has slowed progress. Herein is presented scientific work focused on detecting and quantifying some of these factors, particularly “sociogenic” determinants such as the seven-day week. Through rhythmometric analysis of longitudinal in-home actigraphy, weekly be-havioral patterns were observed in both young adult males (n = 24, mean age = 23.46 years) and older adults with Parkinson’s disease (n = 13 [7 male], mean age = 60.62 years, mean Hoehn & Yahr Stage = 2.31) that evince a seven-day “circaseptan” rhythm of circadi-an and sleep disruption. This is hypothesized to be dependent upon the seven-day calendar week, particularly the regular and abrupt shifts in timing between work and rest days. These perturbations vary by chronotype in young adults, and by disease severity in Parkin-son’s disease. Collectively, these results contribute to the growing evidence that our daily rhythms are shaped by sociogenic factors in addition to well-documented environmental and biological mechanisms. Moreover, the study of these subtle infradian patterns presents serious – yet surmountable – methodological challenges that must be overcome in order to accurately monitor, quantify, analyze, report, and apply findings from observational studies of naturalistic human behavior to scientific and clinical problems

Assessment of Foot Signature Using Wearable Sensors for Clinical Gait Analysis and Real-Time Activity Recognition

Locomotion is one of the most important abilities of humans. Actually, gait locomotion provides mobility, and symbolizes freedom and independence. However, gait can be affected by several pathologies, due to aging, neurodegenerative disease, or trauma. The evaluation and treatment of mobility diseases thus requires clinical gait assessment, which is commonly done by using either qualitative analysis based on subjective observations and questionnaires, or expensive analysis established in complex motion laboratories settings. This thesis presents a new wearable system and algorithmic methods for gait assessment in natural conditions, addressing the limitations of existing methods. The proposed system provides quantitative assessment of gait performance through simple and precise outcome measures. The system includes wireless inertial sensors worn on the foot, that record data unobtrusively over long periods of time without interfering with subject's walking. Signal processing algorithms are presented for the automatic calibration and online virtual alignment of sensor signals, the detection of temporal parameters and gait phases, and the estimation of 3D foot kinematics during gait based on fusion methods and biomechanical assumptions. The resulting 3D foot trajectory during one gait cycle is defined as Foot Signature, by analogy with hand-written signature. Spatio-temporal parameters of interest in clinical assessment are derived from foot signature, including commonly parameters, such as stride velocity and gait cycle time, as well as original parameters describing inner-stance phases of gait, foot clearance, and turning. Algorithms based on expert and machine learning methods have been also adapted and implemented in real-time to provide input features to recognize locomotion activities including level walking, stairs, and ramp locomotion. Technical validation of the presented methods against gold standard systems was carried out using experimental protocols on subjects with normal and abnormal gait. Temporal aspects and quantitative estimation of foot-flat were evaluated against pressure insoles in subjects with ankle treatments during long-term gait. Furthermore, spatial parameters and foot clearance were compared in young and elderly persons to data obtained from an optical motion capture system during forward gait trials at various speeds. Finally, turning was evaluated in children with cerebral palsy and people with Parkinson's disease against optical motion capture data captured during timed up and go and figure-of-8 tests. Overall, the results demonstrated that the presently proposed system and methods were precise and accurate, and showed agreement with reference systems as well as with clinical evaluations of subjects' mobility disease using classical scores. Currently, no other methods based on wearable sensors have been validated with such precision to measure foot signature and subsequent parameters during unconstrained walking. Finally, we have used the proposed system in a large-scale clinical application involving more than 1800 subjects from age 7 to 77. This analysis provides reference data of common and original gait parameters, as well as their relationship with walking speed, and allows comparisons between different groups of subjects with normal and abnormal gait. Since the presented methods can be used with any foot-worn inertial sensors, or even combined with other systems, we believe our work to open the door to objective and quantitative routine gait evaluations in clinical settings for supporting diagnosis. Furthermore, the present studies have high potential for further research related to rehabilitation based on real-time devices, the investigation of new parameters' significance and their association with various mobility diseases, as well as for the evaluation of clinical interventions

Human Motion Analysis with Wearable Inertial Sensors

High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system

A neuroprothesis for tremor management

Tremor is the most common movement disorder, affecting ∼ 15 % of people over 50 years old according to some estimates. It appears due to a number of syndromes, being essential tremor and Parkinson's disease the most prevalent among them. None of these conditions is fully understood. Tremor is currently treated through drugs or neurosurgery, but unfortunately, it is not managed effectively in ∼25 % of the patients. Therefore, it constitutes a major cause of loss of independence and quality of life. Various alternative approaches for tremor management are reported in the literature. Among them, those devices that rely on the application of forces to the tremulous segments show a considerable potential. A number of prototypes that exploit this principle are available, spanning fixed devices and orthoses. However, none of them has fulfilled user's expectation for continuous use during daily living. This thesis presents the development and validation of a neuroprosthesis for tremor management. A neuroprosthesis is a system that restores or compensates for a neurological function that is lost. In this case, the neuroprosthesis aims at compensating the functional disability caused by the tremor. To this end, it applies forces to the tremulous limb through the control of muscle contraction, which is modulated according to the characteristics of the tremor. The concept design envisions the device as a textile that is worn on the affected limb, thus meeting the usability requirements defined by the patients. The development of the neuroprosthesis comprised the following tasks: 1. The development of a concept design of the neuroprosthesis, which incorporates state of the art knowledge on tremor, and user's needs. 2. The design and validation of a cognitive interface that parameterizes the tremor in functional contexts. This interface provides the information that the neuroprosthesis uses for tremor suppression. Two versions are developed: a multimodal interface that integrates the recordings of the whole neuromusculoskeletal system, and an interface incorporating only wearable movement sensors. The latter is intended for the functional validation of the neuroprosthesis, while the former is a proof of concept of an optimal interface for this type of applications. 3. The development of a novel approach for tremor suppression through transcutaneous neurostimulation. The approach relies on the modulation of muscle cocontraction as a means of attenuating the tremor without the need of conventional actuators. The experimental validation here provided demonstrates the feasibility and interest of the approach. In parallel with the validation of the neuroprosthesis, I performed a detailed study on the physiology of motoneurons in tremor, given the lack of a complete description of its behavior. The outcome of this study contributes to the interpretation of the results obtained with the neuroprosthesis, and opens new research lines, both related to alternative interventions and basic neuroscience. In summary, the results here presented demonstrate that tremor may be accurately parameterized while the patient performs functional activities, and that this information may be exploited to drive a neuroprosthesis for tremor management. Furthermore, the novel approach for tremor suppression presented in this dissertation constitutes a potential approach for treating upper limb tremor, either alone, or as a complement to pharmacotherapy. These results encourage the validation of the neuroprosthesis in a large cohort of patients, in order to enable its translation to the market. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El temblor es el trastorno del movimiento más común, afectando, según algunas estimaciones, al ∼15 % de la población de más de 50 años. Existen diversos "síndromes" que causan temblor, siendo el temblor esencial y la enfermedad de Parkinson los que presentan mayor prevalencia. Además, cabe resaltar que no existe una descripción completa de ninguno de ellos. En la actualidad el temblor se trata mediante una serie de fármacos o neurocirugía. A pesar de ello, el ∼ 25 % de los pacientes sufren problemas funcionales debido a su condición. Por tanto, es evidente que el temblor constituye una de las principales causas de dependencia y pérdida de calidad de vida. Realizando una revisión de las publicaciones científicas sobre el temblor, se observa que se ha propuesto un considerable número de tratamientos alternativos. Entre ellos destacan los dispositivos que se fundamentan en la aplicación de fuerzas sobre los segmentos afectados por el temblor, de los que ya se ha evaluado una serie de prototipos. Estos abarcan desde dispositivos fijados a otras estructuras hasta ortesis. Sin embargo, ninguno de ellos satisface las expectativas de los usuarios para su uso durante el día a día. Esta tesis presenta el diseño y validación de una neruoprótesis para el tratamiento del temblor. Una neuroprótesis es un sistema que reemplaza o compensa una función neurológica perdida. En este caso, la neuroprótesis tiene como objetivo compensar la discapacidad motora causada por el temblor. Para ello aplica fuerzas al miembro afectado a través del control del nivel de contracción muscular, que se modula según las características del temblor. El diseño conceptual contempla al dispositivo como un textil que se viste en el brazo afectado, satisfaciendo los requisitos de usabilidad definidos por los pacientes. El desarrollo de la neuroprótesis abarcó las siguientes tareas: 1. El desarrollo del diseño conceptual de la neuroprótesis, que incorpora el conocimiento actual sobre el temblor, y las necesidades de los usuarios. 2. El diseño y validación de una interfaz cognitiva que parametriza el temblor durante tareas funcionales. La información obtenida con esta interfaz es usada por la neuroprótesis para modular la corriente aplicada mediante técnicas de neuroestimulación. Se desarrollan dos versiones de la interfaz cognitiva: una interfaz multimodal que integra información de todo el sistema neuromusculoesquelético, y una interfaz que implementa únicamente sensores vestibles de movimiento. La segunda interfaz fue la que se usó durante la validación funcional de la neuroprótesis, mientras que la primera es una prueba de concepto de una interfaz óptima para este tipo de aplicaciones. 3. El desarrollo de una nueva aproximación para la supresión del temblor mediante neuroestimulación transcutánea. Dicha aproximación se fundamenta en la modulación del grado de co-contracción de los músculos afectados como forma de atenuar el temblor, sin necesidad de usar actuadores convencionales. La evaluación experimental sirvió para demostrar la viabilidad e interés de la intervención. En paralelo a la validación de la neuroprótesis, llevé a cabo un estudio detallado de la fisiología de las motoneuronas en el caso del temblor, dado que no existe una descripción del funcionamiento de las mismas en el caso de este trastorno. Este estudio sirve para ayudar a la interpretación de los resultados de la neuroprótesis, y para abrir una serie de líneas futuras de investigación, tanto sobre nuevas intervenciones para el temblor, como sobre neurociencia básica. En resumen, los resultados que se presentan en esta tesis demuestran que es posible parametrizar de una forma precisa el temblor durante la realización de tareas funcionales, y que esta información sirve para controlar una neuroprótesis para el tratamiento del temblor. Además, la nueva aproximación para la compensación del temblor que se presenta tiene el potencial de convertirse en un tratamiento alternativo para el temblor de miembro superior, ya sea de forma independiente o como complemento a los fármacos. Estos resultados alientan la validación de la neuroprótesis en una cohorte grande de pacientes, con el objetivo de facilitar su transferencia al mercado

Mobile Health Analytics for Senior Care: A Data Mining and Deep Learning Approach

Senior citizens confront numerous challenges to their independent living, including chronic physical health conditions and a decline in mobility. With the advancement of mobile sensing technologies, medical professionals and information systems (IS) researchers have sought to apply data mining techniques to provide precise, prompt, and personalized assessment for falls and health conditions including Parkinson’s disease. Given the societal importance of senior care, my dissertation aims to address the following four research questions: (1) how can we promptly detect senior citizens’ adverse events, e.g., falls, to alleviate consequences, (2) how can we precisely assess senior citizens’ health risks, e.g., fall risks, to provide proper interventions, (3) how can we leverage multiple data sources and assess senior citizens’ health risks in a more holistic manner, and (4) how can we profile senior citizens’ long-term health progression for more personalized care. This dissertation presents four essays to tackle these questions. The essays develop state-of-the-art data mining and deep learning techniques to address selected senior care inquiries. The first essay focuses on a novel hidden Markov model with sensor orientation calibration to detect falls. The second essay presents a two-dimensional heterogeneous convolutional neural network to precisely assess fall risks. The third essay leverages deep multisource multitask learning to achieve sensor fusion and assess multiple health risks and disease severities. The final essay develops an adaptive time-aware convolutional long short term memory model that enables long-term health profiling with time irregularities. Presented frameworks, systems, and design principles not only advance mobile health analytics and deep learning methodologies, but also guide future computational design science research in IS

IMUs: validation, gait analysis and system’s implementation

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a prevalent problem in actual society. The number of falls has been increasing greatly in the last fifteen years. Some falls result in injuries and the cost associated with their treatment is high. However, this is a complex problem that requires several steps in order to be tackled. Namely, it is crucial to develop strategies that recognize the mode of locomotion, indicating the state of the subject in various situations, namely normal gait, step before fall (pre-fall) and fall situation. Thus, this thesis aims to develop a strategy capable of identifying these situations based on a wearable system that collects information and analyses the human gait. The strategy consists, essentially, in the construction and use of Associative Skill Memories (ASMs) as tools for recognizing the locomotion modes. Consequently, at an early stage, the capabilities of the ASMs for the different modes of locomotion were studied. Then, a classifier was developed based on a set of ASMs. Posteriorly, a neural network classifier based on deep learning was used to classify, in a similar way, the same modes of locomotion. Deep learning is a technique actually widely used in data classification. These classifiers were implemented and compared, providing for a tool with a good accuracy in recognizing the modes of locomotion. In order to implement this strategy, it was previously necessary to carry out extremely important support work. An inertial measurement units’ (IMUs) system was chosen due to its extreme potential to monitor outpatient activities in the home environment. This system, which combines inertial and magnetic sensors and is able to perform the monitoring of gait parameters in real time, was validated and calibrated. Posteriorly, this system was used to collect data from healthy subjects that mimicked Fs. Results have shown that the accuracy of the classifiers was quite acceptable, and the neural networks based classifier presented the best results with 92.71% of accuracy. As future work, it is proposed to apply these strategies in real time in order to avoid the occurrence of falls.As quedas são um problema predominante na sociedade atual. O número de quedas tem aumentado bastante nos últimos quinze anos. Algumas quedas resultam em lesões e o custo associado ao seu tratamento é alto. No entanto, trata-se de um problema complexo que requer várias etapas a serem abordadas. Ou seja, é crucial desenvolver estratégias que reconheçam o modo de locomoção, indicando o estado do sujeito em várias situações, nomeadamente, marcha normal, passo antes da queda (pré-queda) e situação de queda. Assim, esta tese tem como objetivo desenvolver uma estratégia capaz de identificar essas situações com base num sistema wearable que colete informações e analise a marcha humana. A estratégia consiste, essencialmente, na construção e utilização de Associative Skill Memories (ASMs) como ferramenta para reconhecimento dos modos de locomoção. Consequentemente, numa fase inicial, foram estudadas as capacidades das ASMs para os diferentes modos de locomoção. Depois, foi desenvolvido um classificador baseado em ASMs. Posteriormente, um classificador de redes neuronais baseado em deep learning foi utilizado para classificar, de forma semelhante, os mesmos modos de locomoção. Deep learning é uma técnica bastante utilizada em classificação de dados. Estes classificadores foram implementados e comparados, fornecendo a uma ferramenta com uma boa precisão no reconhecimento dos modos de locomoção. Para implementar esta estratégia, era necessário realizar previamente um trabalho de suporte extremamente importante. Um sistema de unidades de medição inercial (IMUs), foi escolhido devido ao seu potencial extremo para monitorizar as atividades ambulatórias no ambiente domiciliar. Este sistema que combina sensores inerciais e magnéticos e é capaz de efetuar a monitorização de parâmetros da marcha em tempo real, foi validado e calibrado. Posteriormente, este Sistema foi usado para adquirir dados da marcha de indivíduos saudáveis que imitiram quedas. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e o classificador baseado em redes neuronais apresentou os melhores resultados com 92.71% de precisão. Como trabalho futuro, propõe-se a aplicação destas estratégias em tempo real de forma a evitar a ocorrência de quedas

Automatic Posture Correction Utilizing Electrical Muscle Stimulation

Habitually poor posture can lead to repetitive strain injuries that lower an individual\u27s quality of life and productivity. Slouching over computer screens and smart phones, asymmetric weight distribution due to uneven leg loading, and improper loading posture are some of the common examples that lead to postural problems and health ramifications. To help cultivate good postural habits, researchers have proposed slouching, balance, and improper loading posture detection systems that alert users through traditional visual, auditory or vibro-tactile feedbacks when posture requires attention. However, such notifications are disruptive and can be easily ignored. We address these issues with a new physiological feedback system that uses sensors to detect these poor postures, and electrical muscle stimulation to automatically correct the poor posture. We compare our automatic approach against other alternative feedback systems and through different unique contexts. We find that our approach outperformed alternative traditional feedback systems by being faster and more accurate while delivering an equally comfortable user experience

Online control of a mobility assistance smart walker

Dissertação de mestrado integrado em Engenharia BiomédicaThis work presents the NeoASAS project that was developed at the Bioengineering Group, Consejo Superior de Investigaciones Cientificas (CSIC) in Madrid. Further, it continued with adaptations and improvements at Minho University with the Adaptive System Behavior Group (ASBG) in Guimarães, being designated by ASBGo Project. These developments include the conceptual design, implementation and validation of Smart Walkers with a new interface approach integrated into these devices. This interface is based on a joystick and it is intended to extract the user’s movement intentions. It was designed to be user-friendly and efficient, meeting usability aspects and focused on a commercial implementation, but not being demanding at the user cognitive level. Considering the ASBGo walker, the overall assemblage, mechanical adjustments, electronics and computing have been performed. First, a review about the mobility assistive devices is presented, specially focused on Smart Walkers. Despite the intensive research, in current literature, there are not many works providing a "point of the situation", and explaining the role that robotics can play in this domain. Healthy users performed preliminary sets of experiments with each walker, which showed the sensibility of the joystick to extract command intentions from the user. These signals presented a higher frequency component that was attenuated by a Benedict-Bordner g-h filter, considering the NeoASAS walker and by a Butterworth circuit, considering the ASBGo walker. These methodologies offer a cancelation of the undesired components from joystick data, allowing the system to extract in real-time user’s commands. Based on this identification, an approach to the control architecture based on a fuzzy logic algorithm was developed, in order to allow the control of the walkers’ motors. In addition, a set of sensors were integrated on the walker for safety reasons: an infrared sensor to detect if the user is falling forwards; two force sensors to make sure that the user is properly grabbing the hand support; and two force sensors in the support forearms to verify if the user is with his forearms properly supported. This will make sure that the device stops when one of these situations happens. Thus, an assistive device to provide safety and natural manoeuvrability was conceived and offers a certain degree of intelligence in assistance and decision-making. These results will be used to advance towards a commercial product with an affordable cost, but presenting high reliability and safety. The motivation is that this will contribute to improve rehabilitation purposes by promoting ambulatory daily exercises and thus extend users’ independent living.Este trabalho apresenta o projecto NeoASAS desenvolvido no Grupo de Bioengenharia, do Consejo Superior de Investigaciones Cientificas (CSIC) em Madrid. Este teve continuidade com adaptações e melhorias na Universidade do Minho com o grupo Adaptative System Behaviour (ASBG) em Guimarães, sendo designado por projecto ASBGo. Estes desenvolvimentos incluem o projecto concetual, implementação e validação de andarilhos inteligentes com uma nova interface integrada nestes dispositivos. Esta interface é baseada num joystick e tem como objetivo a extração de intenções de comando do utilizador, sendo intuitiva e eficiente. Atende a aspectos de usabilidade e está focada numa aplicação comercial, não sendo exigente a nível cognitivo. Considerando o andarilho ASBGo, foi realizada a construção deste, bem como, ajustes mecânicos, eletrónicos e programação. É apresentada uma revisão sobre os dispositivos de assistência à marcha, tendo especial enfoque os andarilhos. Apesar da intensa investigação, na literatura não existem trabalhos que apresentem o ponto de situação desta área, bem como o seu papel na robótica de reabilitação. Depois foram realizados testes com utilizadores, mostrando a sensibilidade que o joytick tem na identificação de inteções de comando do utilizador. Além disso, os sinais apresentam uma componente de alta frequência que foi atenuada, no caso do NeoASAS, com um filtro g-h Benedict-Bordner, e no caso do ASBGo, através de um filtro Butterworth implementado em hardware. As metodologias apresentadas oferecem um cancelamento componentes indesejáveis, permitindo ao sistema a extração das intenções de comando do utilizador em tempo real. Desta forma, uma arquitetura de controlo baseada em fuzzy logic foi desenvolvida de maneira a fornecer uma assistência segura ao utilizador, através do controlo dos motores. Foram também integrados um conjunto de sensores no andarilho por razões de segurança: um sensor infravermelho para detetar a queda frontal do utilizador, dois sensores de força nos apoios de mão para detetar se o utilizador está a agarrá-los, e dois sensores de força nos suportes de antebraço para certificar que o utilizador está devidamente apoiado. Assim, foi concebido um dispositivo que garante a segurança do utilizador e oferece um certo grau de inteligência e tomada de decisão. Estes resultados serão utilizados para a criação de um produto comercial com custo acessível, mas com alta confiabilidade. A motivação deste trabalho reflete-se na contribuição que este dispositivo terá na melhoria da reabilitação e desenvolvimento de dispositivos ambulatórios para promover exercicios diários, e melhorar a vida dos utilizadores