Virtual Robot for Interactive Gait Training Improving Regularity and Dynamic Stability of the Stride Patterns

Muto, Takeshi; Herzberger, Barbara; Hermsdoerfer, J.; Poeppel, Ernst; Miyake, Yoichi

doi:10.1109/iccme.2007.4381942

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…This human-machine interaction provides a good example of coupling internal and external systems through dynamic feedback [30] , [32] and is a promising rehabilitation tool. Previous work showed that the interactive system can stabilize gait in hemiparetic stroke patients [50] and in Parkinson's patients with strongly festinating gait [33] . Future work should investigate effectiveness in patients ‘off’ or with reduced dopaminergic medication.…”

Section: Discussionmentioning

confidence: 99%

Interactive Rhythmic Auditory Stimulation Reinstates Natural 1/f Timing in Gait of Parkinson's Patients

et al. 2012

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Parkinson's disease (PD) and basal ganglia dysfunction impair movement timing, which leads to gait instability and falls. Parkinsonian gait consists of random, disconnected stride times—rather than the 1/f structure observed in healthy gait—and this randomness of stride times (low fractal scaling) predicts falling. Walking with fixed-tempo Rhythmic Auditory Stimulation (RAS) can improve many aspects of gait timing; however, it lowers fractal scaling (away from healthy 1/f structure) and requires attention. Here we show that interactive rhythmic auditory stimulation reestablishes healthy gait dynamics in PD patients. In the experiment, PD patients and healthy participants walked with a) no auditory stimulation, b) fixed-tempo RAS, and c) interactive rhythmic auditory stimulation. The interactive system used foot sensors and nonlinear oscillators to track and mutually entrain with the human's step timing. Patients consistently synchronized with the interactive system, their fractal scaling returned to levels of healthy participants, and their gait felt more stable to them. Patients and healthy participants rarely synchronized with fixed-tempo RAS, and when they did synchronize their fractal scaling declined from healthy 1/f levels. Five minutes after removing the interactive rhythmic stimulation, the PD patients' gait retained high fractal scaling, suggesting that the interaction stabilized the internal rhythm generating system and reintegrated timing networks. The experiment demonstrates that complex interaction is important in the (re)emergence of 1/f structure in human behavior and that interactive rhythmic auditory stimulation is a promising therapeutic tool for improving gait of PD patients.

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Section: Discussionmentioning

confidence: 99%

Interactive Rhythmic Auditory Stimulation Reinstates Natural 1/f Timing in Gait of Parkinson's Patients

et al. 2012

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“…For the detection and prevention of falls, some biomedical engineering systems have been developed: (i) robotic prostheses that use electromyographic signals to better synchronize joint movement, such as real-time biofeedback [18]; (ii) interactive systems (virtual robots) that present periodic auditory stimuli and determine a synchronism with the patient's uncoordinated steps [19]; (iii) gait monitoring through pressure sensors built into the shoes [20,21]. Among these processes, there is also the possibility of remote monitoring systems with Wi-Fi and Bluetooth connections [22].…”

Section: Introductionmentioning

confidence: 99%

System for the Analysis of Human Balance based on Accelerometers and Support Vector Machines

Pinheiro

Carmo

Miosso

2020

Preprint

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Background: Disturbances in balance control lead to movement impairment and severe discomfort, dizziness, and vertigo. They can also lead to serious accidents, due to the loss of balance in critical conditions. It is important to monitor the level of balance in order to determine the risk of a fall and to evaluate progress during treatment. Some solutions exist, such as those based on cameras and force platforms, but they are generally restricted to indoor environments. We propose and evaluate a system, based on accelerometers and support vector machines (SVMs), that indicates the user’s postural balance variation by monitoring signals related to balance, and which can be used in indoor and outdoor environments. Results and Discussion: The results show that the system can classify the acquired signals into two to seven levels of balance, with success rates ranging from 92.5% (for seven levels) to 98.3%, in 1000 sessions of random resampling. With two levels of balance, the system attains in the best case an accuracy of 98.9%. The average accuracy with two levels of balance was signiﬁcantly greater than 93% (p=0.045) and the accuracy was signiﬁcantly greater than 97% (p=0.044). With seven levels of balance, the accuracy was signiﬁcantly greater than 94% (p=0.046) and the precision was signiﬁcantly greater than 80% (p=0.049). The tests performed with the dolls show that the system is able to distinguish between the conditions of a sudden drop and of a recovery of balance after losing one’s balance. In this case, the average accuracy was greater than 95% (p=0.043) and the precision was greater than 95% (p=0.026). The system was also able to infer the centroid of each COP region with an error lower than 0.9cm (p=0.0045). These results suggest that the system can be used to detect variations in balance and, therefore, to indicate the risk of a fall even in outdoor environments. Methods: The proposed system consists of a second-skin shirt, six accelerometers, a 328 ATMEGA microcontroller, and a local storage module. For the training phase, we used the accelerometer signals acquired from a single subject under monitored conditions of balance and intentional imbalance, and used the scores provided by a validated commercial solution (the SWAY® software) for establishing the reference target values. Based on these targets, we trained an SVM to classify the signal into n levels of balance (with n varying from 2 to 7) and later evaluated the performance using cross validation by random resampling. We also developed an SVM approach for estimating the center of pressure based on the signals from the accelerometers, by using as reference targets the results from a force platform by AMTI®. We considered ﬁve possible regions for the center of mass, and our system was used to determine the correct region using the accelerometer signals. For validation, we performed experiments with a subject who was ﬁrst standing, and later walking, performing a body rotation, and performing sudden intentional drops. Later the subject was requested to stand and then incline in four main directions, so the diﬀerent centers of pressure (COPs) could be computed by our system and compared to the results from the force platform. We also performed tests with a dummy and a John Doe doll, in order to observe the system’s behavior in the presence of a sudden drop or a lack of balance.

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“…Tais soluções dependem basicamente da habilidade de observação dos profissionais de saúde, conhecimentos básicos e experiência, onde o efeito do tratamento pode ser limitado pela capacidade analítica desses profissionais. Porém, cabe citar outras soluções disponíveis fornecidas pela engenharia biomédica, tais como: (i) próteses robóticas que utilizam sinais eletromiográficos para melhor sincronizar o movimento das articulações, como biofeedback em tempo real [37]; (ii) sistemas interativos (robôs virtuais) que apresentam estímulos auditivos periódicos e determinam um sincronismo com os passos descompassados do paciente [58]; (iii) monitoramento de marcha através de sensores de pressão embutidos nos calçados [47,6]. Dentre esses processos, há também a capacidade de monitoramento dos sistemasà distância com conexões Wi-Fi, Bluetooth [79].…”

Section: Contextualização E Formulação Do Problemaunclassified

“…O modelo Dual-Dynamicsé independente para cada perna do usuário, pois os ritmos das pernas são distintos. O robô tem como objetivo reduzir essa diferença nos ritmos das pernas, dando ao paciente estímulos auditivos rítmicos (via fone de ouvido wireless) [58]. Outro sistemaé o de monitoramento de marchaà base de RFID (Radiofrequency identification) para detecção de tropeços e quedas sem qualquer restrição de tempo ou lugar.…”

Section: Monitoramento Dinâmicounclassified

Desenvolvimento e avaliação de um sistema de análise de equilíbrio postural humano embasado em sinais de inclinômetros e máquinas de suporte vetorial

Pinheiro¹

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Virtual Robot for Interactive Gait Training Improving Regularity and Dynamic Stability of the Stride Patterns

Cited by 8 publications

References 31 publications

Interactive Rhythmic Auditory Stimulation Reinstates Natural 1/f Timing in Gait of Parkinson's Patients

Interactive Rhythmic Auditory Stimulation Reinstates Natural 1/f Timing in Gait of Parkinson's Patients

System for the Analysis of Human Balance based on Accelerometers and Support Vector Machines

Desenvolvimento e avaliação de um sistema de análise de equilíbrio postural humano embasado em sinais de inclinômetros e máquinas de suporte vetorial

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