A low-cost, portable system for the assessment of the postural response of wheelchair users to perturbations

1999 ◽  
Vol 7 (4) ◽  
pp. 435-442 ◽  
Author(s):  
D.G. Kamper ◽  
T.C. Adams ◽  
S.I. Reger ◽  
M. Parnianpour ◽  
K. Barin ◽  
...  
2020 ◽  
pp. 495-501
Author(s):  
Jaya Prasad ◽  
Reema Anne Roy ◽  
Monish Mohan Kora ◽  
Abin Sam ◽  
Chriso Christudhas ◽  
...  

2021 ◽  
pp. 954-960
Author(s):  
Juan Diego Pardo ◽  
Alexander Cerón Correa

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1056 ◽  
Author(s):  
Konstantinos N. Genikomsakis ◽  
Nikolaos-Fivos Galatoulas ◽  
Panagiotis I. Dallas ◽  
Luis Candanedo Ibarra ◽  
Dimitris Margaritis ◽  
...  

2020 ◽  
Vol 134 ◽  
pp. 107486
Author(s):  
Chao Han ◽  
Xiwen He ◽  
Jie Wang ◽  
Lingeng Gao ◽  
Guang Yang ◽  
...  

2017 ◽  
Vol 29 (1) ◽  
pp. 213-223 ◽  
Author(s):  
Reiji Suzuki ◽  
◽  
Shiho Matsubayashi ◽  
Richard W. Hedley ◽  
Kazuhiro Nakadai ◽  
...  

[abstFig src='/00290001/20.jpg' width='300' text='Bird songs recorded and localized by HARKBird' ] Understanding auditory scenes is important when deploying intelligent robots and systems in real-world environments. We believe that robot audition can better recognize acoustic events in the field as compared to conventional methods such as human observation or recording using single-channel microphone array. We are particularly interested in acoustic interactions among songbirds. Birds do not always vocalize at random, for example, but may instead divide a soundscape so that they avoid overlapping their songs with those of other birds. To understand such complex interaction processes, we must collect much spatiotemporal data in which multiple individuals and species are singing simultaneously. However, it is costly and difficult to annotate many or long recorded tracks manually to detect their interactions. In order to solve this problem, we are developing HARKBird, an easily-available and portable system consisting of a laptop PC with open-source software for robot audition HARK (Honda Research Institute Japan Audition for Robots with Kyoto University) together with a low-cost and commercially available microphone array. HARKBird enables us to extract the songs of multiple individuals from recordings automatically. In this paper, we introduce the current status of our project and report preliminary results of recording experiments in two different types of forests – one in the USA and the other in Japan – using this system to automatically estimate the direction of arrival of the songs of multiple birds, and separate them from the recordings. We also discuss asymmetries among species in terms of their tendency to partition temporal resources.


2016 ◽  
Vol 29 (2) ◽  
pp. 61-67 ◽  
Author(s):  
Brandon A. Sherrod ◽  
Dustin A. Dew ◽  
Rebecca Rogers ◽  
James H. Rimmer ◽  
Alan W. Eberhardt

2001 ◽  
Vol 25 (2) ◽  
pp. 96-101 ◽  
Author(s):  
V. Kyriazis ◽  
C. Rigas ◽  
T. Xenakis

An easy-to-use, low cost, portable system is presented. It consists of a transmitter, four electrical sensors, a receiver and a PC with the appropriate software. The system can assess footfall timing, that is the single limb support, double limb support, single step duration values, and the gait cycle duration.This system has been tested for its accuracy with known signals. Then, measurements on a group of twenty (20) healthy adults were performed, with statistically insignificant (p>0.2) results to those reported in the literature. The above prove the system's validity for temporal gait analysis


2018 ◽  
Author(s):  
Giulia Barbareschi ◽  
Catherine Holloway ◽  
Nadia Bianchi-Berthouze ◽  
Sharon Sonenblum ◽  
Stephen Sprigle

BACKGROUND Transfers are an important skill for many wheelchair users (WU). However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories, and these methods of assessment are poorly suited to evaluation in real and unconstrained world settings where transfers take place. OBJECTIVE The objective of this paper is to test the feasibility of a system based on a wearable low-cost sensor to monitor transfer skills in real-world settings. METHODS We collected data from 9 WU wearing triaxial accelerometer on their chest while performing transfers to and from car seats and home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data. RESULTS Results show a good predictive accuracy of support vector machine classifiers when determining the use of head-hip relationship (75.9%) and smoothness of landing (79.6%) when the starting and ending of the transfer are known. Automatic transfer detection reaches performances that are similar to state of the art in this context (multinomial logistic regression accuracy 87.8%). However, we achieve these results using only a single sensor and collecting data in a more ecological manner. CONCLUSIONS The use of a single chest-placed accelerometer shows good predictive accuracy for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous-technology based personalized skill development interventions for WU. However, monitoring transfers still require the use of external inputs or extra sensors to identify the start and end of the transfer, which is needed to perform an accurate evaluation.


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