IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 15, NO. 4, JULY 2011 503

Guest EditorialSensing and Computing in Wearable Robots

AWEARABLE robot is a mechatronic system, to be wornby a person, designed around the shape and functions of

the human body. Research on wearable robots has been pursuedextensively in all parts of the world over the past few years.In medical applications, wearable robots have been developedfor the assistance, rehabilitation, and restoration of physicaland biological functions in people who have lost some bodyfunctions. For example, a limb amputee may wear a prosthesisto restore the motor functions involved in the lost limb, a patientfollowing a stroke may use a wearable robot for rehabilitation oflimb functions, and those with mobility limitations may employwearable assistive devices to enhance their mobility.

Sensing and computing of information that is used to controlor communicate with wearable robots are vital for the perfor-mance and effectiveness of these devices. Physical and biomed-ical information acquired from the human operator has beenapplied for the development of sophisticated wearable robots.In addition, novel neural–machine interfaces offer great hopefor people with quadriplegia and other severe conditions.

The goal of this special session is to present original and rel-evant contributions in the area of information sensing and com-puting for control of or communication with wearable robots inmedical applications.

The session starts with an article by Li et al. [1]. The objectiveof this study is to assess whether there is evidence of spinalmotoneuron loss in the paretic muscles of hemiparetic strokesurvivors using a novel neurophysiological technique.

Upper-limb motion estimation has been regarded as the mostdifficult problem in human motion capture. Two articles in thisspecial session address this problem: Zhang et al. [2] present anovel ubiquitous upper-limb motion estimation algorithm thatoutperforms other previous methods. Similarly, Kwon and Kim[3] propose another new estimation method useful for naturalhuman–machine cooperation control.

The following article, by Vernon and Joshi [4], shows howan electromyographic activity on the surface of a single facemuscle site recorded with a standard electrode can be efficientlyused in brain–muscle–computer interfaces.

Digital Object Identifier 10.1109/TITB.2011.2160245

The last paper of this special session, by Gonzalez-Valenzuelaet al. [5], illustrates a handoff and data-relaying scheme that en-ables continuous monitoring of patients using wearable sensors.

The Guest Editors would like to thank all the authors for theirhigh-quality papers, all the reviewers for their excellent work inevaluating and sharpening the manuscripts, and Dr. F. Lehockifor organizing this special section.

LORENZO TURICCHIA, Guest EditorResearch Laboratory of ElectronicsMassachusetts Institute of TechnologyCambridge, MA 02139 USA

GUANGLIN LI, Guest EditorThe Key Lab for Health Informatics of Chinese Academy

of SciencesShenzhen Institutes of Advanced TechnologyChinese Academy of Sciences, Shenzhen 518055, China

REFERENCES

[1] X. Li, Y.-C. Wang, N. L. Suresh, W. Z. Rymer, and P. Zhou, “Motor unitnumber reductions in paretic muscles of stroke survivors,” IEEE Trans.Inf. Technol. Biomed., vol. 15, no. 4, p. 1, Jul. 2011.

[2] Z.-Q. Zhang, J.-K. Wu, W.-C. Wong, and G.-Z. Yang, “Ubiquitous humanupper limb motion estimation using wearable sensors,” IEEE Trans. Inf.Technol. Biomed., vol. 15, no. 4, Jul. 2011.

[3] S. Kwon and J. Kim, “Real-time upper limb motion estimation from sur-face electromyography and joint angular velocities using an artificial neu-ral network for human-machine cooperation,” IEEE Trans. Inf. Technol.Biomed., vol. 15, no. 4, Jul. 2011.

[4] S. Vernon and S. S. Joshi, “Brain–muscle–computer interface: Mobile-phone prototype development and testing,” IEEE Trans. Inf. Technol.Biomed., vol. 15, no. 4, Jul. 2011.

[5] S. Gonzalez-Valenzuela, M. Chen, and V. C. M. Leung, “Mobility supportfor health monitoring at home using wearable sensors,” IEEE Trans. Inf.Technol. Biomed., vol. 15, no. 4, Jul. 2011.

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504 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 15, NO. 4, JULY 2011

Lorenzo Turicchia received the Laurea degree in electrical engineering from the University ofPadova, Padua, Italy, and the Ph.D. degree in computer science from the Department of Mathe-matics and Computer Science, University of Udine, Udine, Italy.

He is a Research Scientist in the Research Laboratory of Electronics at the MassachusettsInstitute of Technology (MIT), Cambridge. In 2002, he joined the Analog VLSI and BiologicalSystems group at MIT, where he completed his doctoral research and is now a Research Sci-entist. His main research interests include nonlinear signal processing, machine learning, andbioelectronics. His research interests also include cochlear implants for the hearing impaired,visual prostheses for the blind, speech prostheses for individuals with severe communicationdisabilities, automatic speech recognition in noise, and wearable medical devices. In these areas,he has authored 7 patent applications and more than 40 publications. He is currently working onrobust techniques for the recognition of speech, speaker, and language in noisy environments;bioelectronics for wearable and implantable medical devices; and neural decoding techniques for

neural prosthetic devices for the paralyzed. He is an Associate Editor of the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY

IN BIOMEDICINE and is associated with the program committees of several technical conferences.

Guanglin Li (M’01–SM’06) received the B.S. and the M.S. degrees both in electrical engineeringfrom Shandong University, Jinan, China, in 1983 and 1988, respectively, and the Ph.D. degree inbiomedical engineering from Zhejiang University, Hangzhou, China, in 1997.

He was an Associate Professor with the Department of Automatic Control Engineering, Shan-dong University in 1998. From 1999 to 2002, he was a Research Fellow, and later a PostdoctoralResearch Associate, in the Department of Bioengineering, University of Illinois, Chicago. From2002 to 2006, he was a Senior Research Scientist at BioTechPlex Corporation. From 2006 to2009, he was a Senior Research Scientist in the Neural Engineering Center for Artificial Limbsat the Rehabilitation Institute of Chicago and, also, a Research Assistant Professor in the Depart-ment of Physical Medicine and Rehabilitation at Northwestern University, Chicago. Since 2009,he has been with Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy ofSciences, Shenzhen, China, where he is currently a Professor in the Research Centre for NeuralEngineering at the Institute of Biomedical and Health Engineering. His current research interests

include neural rehabilitation engineering, neuroprosthesis control, biomedical signal analysis, and computational biomedical en-gineering. He is an Associate Editor of the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE and serves asan International Advisory Board member of journal of Physiological Measurement.