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Carlos A. Cifuentes G. - Citácie služby Študovňa Google
Our Day return guarantee still applies. Bookseller Completion Rate This reflects the percentage of orders the seller has received and filled. Advanced Book Search Browse by Subject. As a result, a third component related to user's navigation commands is distinguished. For the cancelation of high-frequency noise, a Benedict-Bordner g-h filter was designed presenting very low values for Kinematic Tracking Error 2. This was done without compromising the information contained in the frequencies close to such notch filters. The presented methodology offers an effective cancelation of the undesired components from force data, allowing the system to extract in real-time voluntary user's navigation commands.
Based on this real-time identification of voluntary user's commands, a classical approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion. Walkers are designed to assist pathological gait, helping in balance, and providing weight support to the user. Moreover, as walkers rely on the user's ability to walk, these devices play an important role in empowering user's rehabilitation. Walker-assisted gait is a theme of interest in the scientific community. Studies regarding walker assisted gait are found in [ 1 — 3 ].
- Extraction of user's navigation commands from upper body force interaction in walker assisted gait.
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Conventional walkers are prescribed according to certain user's characteristics:. Standard or four-legged wakers are useful for patients with poor balance, [ 4 ], or for those that require some level of partial body weight support PBWS , at the cost of compromising gait patterns and posture during gait.
Upper body strength and good motor coordination are demanded for lifting up and placing forward the device during gait, [ 5 ].
Rollators or four-wheeled walkers offer more natural gait patterns but lack in stability. If users should put much weight on the device, it may roll away, resulting in a fall.
In that context, rollators should be used by patients that require minimal weight bearing, such as individuals with mild to moderate Parkinson's disease or ataxia, [ 5 ]. The advances in technology make possible the incorporation of sensors and actuators in such devices, bringing to these devices new characteristics: improved the therapies based on walkers by means of assist-as-needed intervention and improved device's reliability.
These new characteristics extended the use of walkers to a more diverse population. Sensors aimed at extracting user or environment conditions provide safe and efficient control. Some examples of the most significant smart walkers in the literature are found in [ 6 — 11 ]. A review regarding such devices along with a functional classification was presented in [ 12 ]. In the framework of Simbiosis Project, a robotic walker equipped with a multimodal user-machine interface was developed, [ 12 ]. This work presents a new method for the extraction of user's navigation commands from upper-body force interaction in walker assisted gait.
After a previous analysis of the force sensor data measured in walker's handles, the main components were identified. These components can be attenuated by improving the device's structure. Nevertheless, in outdoors the pavement usually presents imperfections. This requires the development of efficient techniques to remove these components from force data. Second, a component related to user's trunk oscillations, and consequently to user's gait, is observed.
In previous works, [ 13 ], such component was characterized and continuously monitored in order to infer gait parameters from force data.
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- Carlos A. Cifuentes G. - Citations Google Scholar.
- 1. Introduction.
Nevertheless, in this work the focus is on the third component related to user's navigation commands. It is fundamental to infer such commands from the interaction with the robotic walker for an efficient control of the device during assisted gait. This paper presents a filtering architecture and its validation for obtaining user's navigation commands. Section 2 includes a brief presentation of the Simbiosis walker, the force measurement configuration and, most importantly, a discussion regarding the filtering designed to extract the components related to user's navigation commands.
In section 3, the experimental results are presented along with the corresponding discussion.
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Finally, section 4 presents the conclusions and future work. The robotic walker developed under the framework of the Simbiosis Project presents a series of sensor subsystems designed for the acquisition of gait parameters and for the characterization of the human-robot interaction during gait, [ 12 ]. One of them, the upper-body force interaction subsystem , is based on two tridimensional 3D force sensors installed under the forearm supporting platforms, Figure 1. The biaxial sensors are used for measuring the X lateral direction and Y advance direction components. The load cells measure the Z component vertical direction.
When data storage for offline studies is required, a laptop computer is also introduced into the system's architecture. The laptop PC also adds the possibility to control the system externally through a wireless LAN remote desktop connection. The typical force data acquired on the y axis of one of the force sensors is presented in Figure 2. As it can be seen, in the instants that the subject is not walking but has his arms resting on the forearm supports yellow area in Figure 2 , no high frequency noise is observed.
Human-Robot Interaction Strategies for Walker-Assisted Locomotion
This indicates that the high frequency components are generated during the movement of the device. As observed by the authors, such noise is caused by vibrations introduced by both irregularities on the ground and imperfections on the surface of the walker's wheels. Typical raw force data obtained from the Y-axis of the 3D sensor. Two main zones highlighted: i the yellow zone indicates the moments in which the subject has his arms resting on the structure of the walker and is not walking, and ii denotes the moments in which the subject is actually walking with the device.
In addition, during the moments in which the subject is walking blue highlighted area , slower oscillations are also observed in all axis of force data. In previous works, the authors demonstrated that this oscillatory component is specially observed in the vertical direction of the force data, and is a result of the lateral displacements of user's trunk, [ 14 ].
Such oscillations are translated into forearm reactions as the user is supported by the walker. Movements of user's trunk and, consequently, user's center of gravity CoG are highly correlated with gait phases, [ 15 ]. In [ 13 ], the authors proposed a methodology for the extraction of gait parameters, such as heel-strike, toe-off and cadence, from this force component. Finally, transient events related to user's navigation commands are also found within the force sensor data.
The main objective for the installation of force sensors in walker's handles was the identification and characterization of user's guidance intentions.