Abstract-WDD We designed, built, and pilot-tested hardware and software that used a robot to provide muscle reeducation movement patterns after stroke. This is a report on a field trial, in which 11 occupational therapists used the system with 22 patients; each patient averaged 2.2 sessions. Based on information contained in the system database, a log, patient interviews, and therapist questionnaires, we evaluated safety, system utility, and patient and therapist acceptance. The results suggest that robotic treatment is safe and accepted (if not welcomed) by patients. The therapists expressed a qualified acceptance, suggesting several modifications to increase utility. The potential for the application of robotics in rehabilitation therapy is discussed in light of these findings.

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A B s m A mOne way to autaMtically detect periods of sleep or hypcpnea in a respiratory airflcw reocadirg is to first segment the airflcrw waveform i n t o individul breaths.This is difficult if the waveform has a high degree of artifact or a wide range of breath magnitudes.A nonparmnetric statistic applied to a digitized airflw waveform is used to create twu new "trend" wavf2fom whose properties make it easier to analyze t+in the original t i m e damin waveform. certavl wlell defined points a l m g the trend wavefornrs cOrreSpOnd to the beginning and end of individual breaths within the airflcrw wavefonn. lNlRouJcrIoN For a sleeping individual, intends of apnea and hypqmea can be identified by recozding respiratory airflaw at the level of the nose and mouth. 'Ihis can e done with properly positioned MsdL/oral therrmstors [l]. Manual analysis of this type of recording is time and subject to human error so an autcanatic techuque is preferred.To autclmaticaZly rf?cosnize apnea and h m , a computer would fust segment the ~gitized respiratory airflcrw waveform into its individual constituent bmths. Individual breaths are often difficult to recognize because the depth of respiration varies over a wide range and the respiratory waveform is often artifactudl [2]. prwiaus breath detection techniques have varied in caplexity f m s-le single-threshold magnitude ccpnparisons to caplex heuristic @-detecting algorithm using Ntiple arbitrary magnitude and duration thresholds [2,3]. Ihe method described in this paper uses a nonparametric statistic to identify change-points along the respiratory waveform that mrreqmn3 to shifts between inspiration and expiration thereby segmenting the airflm waveform into discrete breaths. MGIHOD Given a seqence of respiratozy airflm q l e s yi, i=1, 2, -* -changepoints c a w at the transitions b e t w e e n '~ and trends. TO detect these trends, the waveform samples are o m z e d into N-tuple~ 'Ihe N-tuple i s divided %half (assumbq N is an even mmbr). If the 3 N-tuple represents no trend (the null hypothesis, Q) then where m = 1+(N/2) and P(Y1 = Ym) = 0 If T is a rardan variable equal to the m m b r of times y is less than ym then T has a bincmial distrdion b(n,1/2) where n is the nunber of untied pairs (ylrym). Given this, dcrcJlward and upward *values equal to 1 minus the probability of camut a type I1 error 141 can be calculated for the j@-tuple as whexe (7. NI-1 1 for YI * Y m 1 = 1 'k+j, ((I-I)N)+b+j with '8.m = 1 0 for y l = y, k-0 Z (A Rotionol Number) L 1/2 s.t. 1 L N.2 ( INT[N/2] and where nI I Z As Defined Above Equations (1) and (2) are generalized f o m of 0330--1EEE ENGINEERING IN MEDICINE L BIOLOGY SOCIETY llTH ANNUAL INTERNATIONAL CONFERENCE

This paper presents and discusses a variety of robotic and mechatronic system applications that deviate from traditional rehabilitation uses, augmenting or replacing lost functional abilities, to applications which use these systems as therapeutic tools serving as part of the rehabilitation, vocational therapy, and educational process. These applications will include those systems designed and developed by the Enabling Technologies Laboratory at Wayne State University, as well as other organizations. Themes are emerging from the experiences gained through these diverse applications. Robotidmechatronic systems promote active participation by the client. They are finding applications where consistent, repeatable manipulative operations are required for extended periods of time. The roles played by therapists, teachers, and service providers change as the role of the technology changes. Service providers are freed from mundane nontherapeutic or noneducational activities, and consequently are able to focus more fully on the client and the therapeutic or educational tasks. The systems have characteristics that improve their cost/effectiveness. For example, therapists or service providers can often leverage their time and attention by accomplishing multiple objectives and/or serving multiple clients with a given therapeutic or educational task. The systems typically perform multiple functions. Therapeutic activity is generally billable, and therefore the technology represents a revenue source. Since the technology is used by a number of clients, its cost can be amortized over this client base. Lastly, clients tend to enjoy working with the technology. Each application is different, but in general, the systems are providing clients the positive effects of competent participation in a task or activity. These applications present system uses that go beyond augmentation and replacement of lost functional abilities and, as such, are expanding the popular model, or paradigm, regarding robotic and mechatronic system applications.

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