Monitoring Walker Assistive Devices: A Novel Approach Based on Load Cells and Optical Distance Measurements

This paper presents a measurement system intended to monitor the usage of walker assistive devices. The goal is to guide the user in the correct use of the device in order to prevent risky situations and maximize comfort. Two risk indicators are defined: one related to force unbalance and the other related to motor incoordination. Force unbalance is measured by load cells attached to the walker legs, while motor incoordination is estimated by synchronizing force measurements with distance data provided by an optical sensor. The measurement system is equipped with a Bluetooth link that enables local supervision on a computer or tablet. Calibration and experimental results are included in the paper.info:eu-repo/semantics/publishedVersio

A preliminary strategy for fall prevention in the ASBGo smart walker

Fall-related injuries affect a large part of the population and related costs. Thus, there is a concern in studying a fall prevention strategy to minimize the consequences of falls. Walkers are assistive devices used to improve the balance, stability and reduce the load on the lower limb of the user. In this sense, there is a concern to improve the safety in smart walkers and, consequently, to prevent falls in these devices. However, in this field, the only approach is to stop the walker in risk situations. So, the aim of this paper is to define a preliminary strategy to prevent a fall event in the Adaptive System Behaviour Group (ASBGo) Smart Walker. For ASBGo Smart Walker, two modes of security are discussed in this paper. One approach is based on monitoring the center of mass and changing the trajectory when a near fall is detected. The other mechanism consists only in to stop the walker when a dangerous situation is detected. The first or the second mode are activated depending if the user drives the walker with the forearm on forearm support or not.This work has been supported by the FCT – Fundação para a Ciência e Tecnologia - with the scholarship reference PD/BD/141515/2018, by the FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project EML under Grant POCI-01-0247-FEDER-033067, and through the COMPETE 2020 - POCI - with the Reference Project under Grant POCI-01-0145-FEDER-00694

First advances in near fall detection and prediction when using a walker

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a major concern to society. Several injuries associated with falls need medical care, and in the worst-case scenario, a fall can lead to death. These consequences have a high cost for the population. In order to overcome these problems, a diversity of approaches for detection, prediction, and prevention of falls have been tackled. Walkers are often prescribed to subjects who present a higher risk of falling. Thus, it is essential to develop strategies to enhance the user's safety in an imminent danger situation. In this sense, this dissertation aims to develop a strategy to detect a near fall (NF) and its direction as well as the detection of incipient near fall (INF) while the subject uses a walker. Furthermore, it has the purpose of detecting two gait events, the heel strike (HS) and the toe-off (TO). The strategies established, in this work, were based on the data gathered through an inertial sensor placed on the lower trunk and force sensors placed on the insoles. Following data collection, the methodology adopted to identify the situations aforementioned was based on machine learning algorithms. In order to reach the model with best performance, many combinations of different classifiers were tested with three feature selection methods. Regarding the detection of NF, the results achieved presented a Matthews Correlation Coefficient (MCC) of 79.99% being possible to detect a NF 1.76±0.76s before its end. With the implementation of the post-processing algorithm, a large part of the false positives was eliminated being able to detect all NF 1.48±0.68s before its end. Concerning the models built to distinguish the direction of the NF, the best model presented accuracy of 58.97% being unable to reliably distinguish the three fall directions. The methodology followed, in this work, was unsuccessful to detect an INF. The best model presented MCC=23.87%, in this case. Lastly, with respect to the detection of HS and TO events the best model reached MCC=86.94%. With the application of the post-processing algorithm, part of misclassified samples was eliminated, however, a delay in the detection of the HS and TO events was introduced. With the post-processing it was possible to reach MCC=88.82%, not including the imposed delay.As quedas representam uma grande preocupação para a sociedade. Várias lesões associadas às quedas necessitam de cuidados médicos e, no pior dos casos, uma queda pode levar à morte. Estas consequências traduzem-se em custos elevados para a população. A fim de ultrapassar estes problemas, várias abordagens têm sido endereçadas para deteção, previsão e prevenção das quedas. Os andarilhos são muitas vezes prescritos a sujeitos que apresentam um risco de queda maior. Desta forma, é essencial desenvolver estratégias para aumentar a segurança do utilizador perante uma situação de perigo iminente. Neste sentido, esta dissertação visa desenvolver uma estratégia que permita a deteção de uma quase queda (NF) e a sua direção, assim como a deteção incipiente de uma NF (INF). Para além disso, tem o objetivo de detetar dois eventos de marcha, o heel strike (HS) e o toe-off (TO). As estratégias definidas, neste trabalho, basearam-se nos dados recolhidos através de um sensor inercial posicionado no tronco inferior e de sensores de força colocados nas palmilhas. Após a aquisição dos dados, a metodologia adotada para identificar as situações anteriormente referidas foi baseada em algoritmos de machine learning. Com o intuito de obter o modelo com melhor desempenho, várias combinações de diferentes classificadores foram testadas com três métodos de seleção de features. No que concerne à deteção da NF, os resultados alcançados apresentaram um Matthews Correlation Coefficient (MCC) de 79.99% sendo possível detetar uma NF 1.76±0.76s antes do seu final. Com a implementação do algoritmo de pós-processamento, grande parte dos falsos positivos foram eliminados, sendo possível detetar todas as NF 1.48±0.68s antes do seu final. Em relação aos modelos construídos para distinguir a direção da NF, o melhor modelo apresentou uma precisão (ACC) de 59.97%. A metodologia seguida neste trabalho não foi bem sucedida na deteção INF. O melhor modelo apresentou um MCC=23.87%. Relativamente à deteção dos eventos, HS e TO, o melhor modelo atingiu um MCC=86.94%. Com a aplicação do algoritmo de pós-processamento parte das amostras mal classificadas foram eliminadas, no entanto, foi introduzido um atraso na deteção do HS e do TO. Com o pós-processamento foi possível obter um MCC=88.82%, não incluindo o atraso imposto pelo pós-processamento

Battery storage integration in voltage unbalance and overvoltage mitigation control strategies and its impact on the power quality

The increased utilisation of distributed renewable energy sources in low voltage grids leads to power quality problems such as overvoltages and voltage unbalance. This imposes challenges to the distribution system operators to maintain the power quality in their grids. To overcome these issues, energy storage systems could be integrated together with the distributed energy resources and the stored energy could be used when needed to better improve power quality and achieve better grid performance. However, integrating an energy storage system introduces additional cost, therefore, determining the right capacity is essential. In this article, an energy storage system is combined with the classical positive-sequence control strategy and the three-phase damping control strategy. The three-phase damping control strategy is able to mitigate the voltage unbalance by emulating a resistive behaviour towards the zero- and negative-sequence voltage components. This resistive behaviour can be set on different values such that the desired voltage unbalance mitigation is achieved. Hence, the three-phase damping control strategy, equipped with the energy storage system is investigated under different values of the resistive behaviour. Both control strategies are investigated under the same conditions and the impact of the different capacities of the energy storage systems is investigated

Navigation system using passive collaborative control adapted to user profile for a rollator device

In order to achieve this goal, research in different areas has been necessary. First, a methodology to provide human-like platform motion in reactive navigation algorithms has been proposed to improve user acceptance of help. Then, work has focused on gait analysis and user's condition estimation using only onboard sensors. In addition, a new methodology to evaluate fall risk using only onboard sensors while users walk has been proposed to balance the contribution of user and robot to control. All proposed subsystems have been validated with a set of volunteers at two rehabilitation hospitals: Fondazione Santa Lucia (Rome) and Hospital Regional Universitario (Malaga). Volunteers presented a wide variety of physical and cognitive disabilities. Tests with healthy volunteers have been discarded from the beginning to avoid a sampling bias error. Obtained results have shown that the proposed system can be used for: i) reactively generating human-like trajectories that outperforms all other tested algorithms in terms of likeness to human paths and success rate; ii) monitoring gait and user's condition while users walk using only on-board sensors; and iii) evaluating fall risk without wearable sensors nor ambient sensors. This thesis open a number of open research lines: i) user condition estimation can be extended to another medical scales; ii) the method to reactively generate human-like-trajectories can be extended to add deliberative human-adapted-path-planning; and iii) the fall risk estimator can be extended to a fall risk predictor.Rollators provide autonomy to persons with mobility impairments. These platforms can be used while people perform their Activities of Daily Living in order to provide support and/or balance. Also, they can be used during the rehabilitation process to strengthen the lower limbs or to provide balance before users can progress to canes or crutches. Rollators have a limited set of personalization options, but they are usually related to the users' body size. Hence, people who need extra typically have to choose a wheelchair instead. This transition to a wheelchair limits users' movements and it increases their disuse syndrome because they do not exercise their lower limbs. Hence, it is a priority to extent the use of rollator platforms as much as possible by adapting help to people who can not use a conventional rollator on their own. Technological enhancements can be added to rollator to expand their use to a larger population. For example, force sensors on handlebars provide information about users' weight bearing. This information can be used during rehabilitation to control their partial weight-bearing. Encoders on wheels may also provide useful information about the walking speed, which is a well know estimator of fall risk. In addition to monitorization, motors can be attached to the wheels for assistance, e.g. to reduce effort while ascending slopes. This thesis focuses on creating a navigation system for a robotized rollator, which includes weight bearing sensors, encoders and wheel motors. The navigation system relies on passive collaborative control to continuously combine user and system commands in a seamless way. The main contribution of this work is adaptation to the user's needs through continuous, transparent monitorization and profile estimation

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

Soft Continuum Robotic Airbag Integrated with Passive Walker for Fall Mitigation

This thesis describes the prototype and development of a soft continuum robotic airbag system that is attached to a passive mobility walker. The system can deploy in multiple configurations: to the front, left, or right of the walker depending on the direction of a detected fall. The continuum component of the project is made of nylon fabric with thin cables, allowing it to be compactly stored before deploying. The airbag is inflated in real time during a fall using a novel compression system. Results of experiments with the prototype in each configuration are presented. The system deploys consistently across falls, significantly reducing the g-force of impact

Motor patterns evaluation of people with neuromuscular disorders for biomechanical risk management and job integration/reintegration

Neurological diseases are now the most common pathological condition and the leading cause of disability, progressively worsening the quality of life of those affected. Because of their high prevalence, they are also a social issue, burdening both the national health service and the working environment. It is therefore crucial to be able to characterize altered motor patterns in order to develop appropriate rehabilitation treatments with the primary goal of restoring patients' daily lives and optimizing their working abilities. In this thesis, I present a collection of published scientific articles I co-authored as well as two in progress in which we looked for appropriate indices for characterizing motor patterns of people with neuromuscular disorders that could be used to plan rehabilitation and job accommodation programs. We used instrumentation for motion analysis and wearable inertial sensors to compute kinematic, kinetic and electromyographic indices. These indices proved to be a useful tool for not only developing and validating a clinical and ergonomic rehabilitation pathway, but also for designing more ergonomic prosthetic and orthotic devices and controlling collaborative robots