1,354 research outputs found
Empowering and assisting natural human mobility: The simbiosis walker
This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
A flexible sensor technology for the distributed measurement of interaction pressure
We present a sensor technology for the measure of the physical human-robot interaction pressure developed in the last years at Scuola Superiore Sant'Anna. The system is composed of flexible matrices of opto-electronic sensors covered by a soft silicone cover. This sensory system is completely modular and scalable, allowing one to cover areas of any sizes and shapes, and to measure different pressure ranges. In this work we present the main application areas for this technology. A first generation of the system was used to monitor human-robot interaction in upper- (NEUROExos; Scuola Superiore Sant'Anna) and lower-limb (LOPES; University of Twente) exoskeletons for rehabilitation. A second generation, with increased resolution and wireless connection, was used to develop a pressure-sensitive foot insole and an improved human-robot interaction measurement systems. The experimental characterization of the latter system along with its validation on three healthy subjects is presented here for the first time. A perspective on future uses and development of the technology is finally drafted
Instrumentation and validation of a robotic cane for transportation and fall prevention in patients with affected mobility
Dissertação de mestrado integrado em Engenharia Física, (especialização em Dispositivos, Microssistemas e Nanotecnologias)O ato de andar é conhecido por ser a forma primitiva de locomoção do ser humano, sendo que este
traz muitos benefícios que motivam um estilo de vida saudável e ativo. No entanto, há condições de saúde
que dificultam a realização da marcha, o que por consequência pode resultar num agravamento da saúde,
e adicionalmente, levar a um maior risco de quedas. Nesse sentido, o desenvolvimento de um sistema de
deteção e prevenção de quedas, integrado num dispositivo auxiliar de marcha, seria essencial para reduzir
estes eventos de quedas e melhorar a qualidade de vida das pessoas. Para ultrapassar estas necessidades
e limitações, esta dissertação tem como objetivo validar e instrumentar uma bengala robótica, denominada
Anti-fall Robotic Cane (ARCane), concebida para incorporar um sistema de deteção de quedas e um
mecanismo de atuação que possibilite a prevenção de quedas, ao mesmo tempo que assiste a marcha.
Para esse fim, foi realizada uma revisão do estado da arte em bengalas robóticas para adquirir um
conhecimento amplo e aprofundado dos componentes, mecanismos e estratégias utilizadas, bem como os
protocolos experimentais, principais resultados, limitações e desafios em dispositivos existentes.
Numa primeira fase, foi estipulado o objetivo de: (i) adaptar a missão do produto; (ii) estudar as
necessidades do consumidor; e (iii) atualizar as especificações alvo da ARCane, continuação do trabalho de
equipa, para obter um produto com design e engenharia compatível com o mercado. Foi depois estabelecida
a arquitetura de hardware e discutidos os componentes a ser instrumentados na ARCane. Em seguida foram
realizados testes de interoperabilidade a fim de validar o funcionamento singular e coletivo dos componentes.
Relativamente ao controlo de movimento, foi desenvolvido um sistema inovador, de baixo custo e
intuitivo, capaz de detetar a intenção do movimento e de reconhecer as fases da marcha do utilizador. Esta
implementação foi validada com seis voluntários saudáveis que realizaram testes de marcha com a ARCane
para testar sua operabilidade num ambiente de contexto real. Obteve-se uma precisão de 97% e de 90% em
relação à deteção da intenção de movimento e ao reconhecimento da fase da marcha do utilizador.
Por fim, foi projetado um método de deteção de quedas e mecanismo de prevenção de quedas para
futura implementação na ARCane. Foi ainda proposta uma melhoria do método de deteção de quedas, de
modo a superar as limitações associadas, bem como a proposta de dispositivos de deteção a serem
implementados na ARCane para obter um sistema completo de deteção de quedas.The act of walking is known to be the primitive form of the human being, and it brings many benefits
that motivate a healthy and active lifestyle. However, there are health conditions that make walking difficult,
which, consequently, can result in worse health and, in addition, lead to a greater risk of falls. Thus, the
development of a fall detection and prevention system integrated with a walking aid would be essential to
reduce these fall events and improve people quality of life. To overcome these needs and limitations, this
dissertation aims to validate and instrument a cane-type robot, called Anti-fall Robotic Cane (ARCane),
designed to incorporate a fall detection system and an actuation mechanism that allow the prevention of
falls, while assisting the gait. Therefore, a State-of-the-Art review concerning robotic canes was carried out to
acquire a broad and in-depth knowledge of the used components, mechanisms and strategies, as well as
the experimental protocols, main results, limitations and challenges on existing devices.
On a first stage, it was set an objective to (i) enhance the product's mission statement; (ii) study the
consumer needs; and (iii) update the target specifications of the ARCane, extending teamwork, to obtain a
product with a market-compatible design and engineering that meets the needs and desires of the ARCane
users. It was then established the hardware architecture of the ARCane and discussed the electronic
components that will instrument the control, sensory, actuator and power units, being afterwards subjected
to interoperability tests to validate the singular and collective functioning of cane components altogether.
Regarding the motion control of robotic canes, an innovative, cost-effective and intuitive motion
control system was developed, providing user movement intention recognition, and identification of the user's
gait phases. This implementation was validated with six healthy volunteers who carried out gait trials with
the ARCane, in order to test its operability in a real context environment. An accuracy of 97% was achieved
for user motion intention recognition and 90% for user gait phase recognition, using the proposed motion
control system.
Finally, it was idealized a fall detection method and fall prevention mechanism for a future
implementation in the ARCane, based on methods applied to robotic canes in the literature. It was also
proposed an improvement of the fall detection method in order to overcome its associated limitations, as
well as detection devices to be implemented into the ARCane to achieve a complete fall detection system
Estimation of posture and joint angle of human body using foot pressure distribution: Morphological computation with human foot
This paper proposes a novel contact and wearable sensing system for
estimating the upper body posture and joint angles (ankle, knee, and hip) of
the human body using foot pressure distribution information obtained from a
sensor attached to the plantar region. In the proposed estimation method,
sensors are installed only on the plantar region, which is the end of the human
body and the point of contact with the environment. The posture and joint
angles of other parts of the body are estimated using only this information. As
a contact and wearable sensor, the proposed system differs from previous
measurement systems in the sense that the sensor does not need to be placed
near the target joint or body. The estimation was carried out using a
multivariate linear regression model with the foot pressure distribution as the
input and the joint angle or posture as the output. The results reveal that it
is possible to estimate the posture and joint angles of the human body from
foot pressure distribution information (R20.9). The proposed
estimation method was validated by morphological computation to confirm that it
is enabled by foot morphology. The validation approach compared the estimation
accuracy achieved when an object was interposed between the foot pressure
distribution sensor and the plantar region and the morphological relationship
of the plantar region to the environment varied. The results reveal that there
is a significant difference in the estimation accuracy between cases with and
without an intervening object, suggesting that the morphology of the plantar
region contributes to the estimation. Furthermore, the proposed estimation
method is considered as physical reservoir computing, wherein the human foot is
used as a computational resource
A cane-based low cost sensor to implement attention mechanisms in telecare robots
Telepresence robots have been recently used for
Comprehensive Geriatric Assessment (CGA). Since the robot
can not track a person continuously, there are several strategies
to decide when to check them, from cyclic checks to simple
requests from users and/or caregivers. In order to adapt to the
user needs and condition, it is preferable to perform CGA as
soon as regularities appear. However, this requires detection
of potential issues in users to offer immediate service. In this
work we propose a new low cost force sensor system to detect
user’s condition and attract attention of CGA robots, so they
can perform a full examination on a need basis. The main
advantages of this system are: i) it can be attached to any
standard commercial cane; ii) its power consumption is very
reduced; and iii) it provides continuous information as long as
the user walks. It has been tested with several elderly volunteers
in care facilities. Results have proven that the sensor readings
are indeed correlated with the users’ condition.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
Fall prevention intervention technologies: A conceptual framework and survey of the state of the art
In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.The Royal Society, grant Ref: RG13082
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