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Guest Editorial Enabling Technologies for Parkinson’s Disease Management ARKINSON’s disease (PD) is the most common neurological movement disorder with a prevalence of up to 2% in the elderly. The cardinal motor symptoms bradykinesia, rigidity, tremor, and postural instability define the diagnosis of the PD [1], [2]. These motor features make PD an ideal model disease to establish, validate, and clinically apply sensor-based movement diagnostic approaches. The Unified PD rating scale is the most widely used clinical score to rate symptoms, in particular, motor symptoms (UPDRS Part III). Measurement of the motor symptoms provides a major treatment target parameter, but high interrater reliability reported for the standardized clinical assessment is a problem [3], which could be improved with sensor-based measurements. Patients typically live with this disease for decades and the motor symptoms gradually increase during disease progression; thereby, constantly adding to a reduced quality of life. In the advanced stages, motor symptoms fluctuate during the course of a day, generating a specific need for the home-based monitoring. Clinical assessments throughout the course of the disease consume substantial resources and frequently repeated assessments are generally impractical, only performed as a gold standard for some drug studies and, still providing only a snap-shot of the patients’ daily life impairments. From a clinical point of view, three different general applications of the sensor-based movement diagnostics are feasible and could be useful. First, instrumented versions of the tests that are already used as a part of the standardized motor function analysis performed by the physician could help reduce the interrater variability. Second, patients can be guided by a clinician through a video connection in their own homes. It could greatly improve the quality of the assessment to have instrument-defined movements evaluated in this semisupervised (or even unsupervised) environment and permit semicontinuous quantitative assessment of the impairment in a home monitoring approach. Finally, continuous analysis of the motor movements during everyday living could allow an unobtrusive assessment and monitoring under real-life conditions. This objective information on the quality of life and daily functioning could complement the diagnostic workup to greatly enhance the disease management and precisely address the patients’ needs, ultimately also reducing the healthcare costs. Developments in the field of portable sensors are being increasingly introduced into clinical diagnostics and treatment of movement disorders such as PD [4]. Improved sensors and algorithms are not only able to simply measure the distinct movement impairment, but also become available to solve clinical questions and to address the patients’ needs [5]. This progress
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Digital Object Identifier 10.1109/JBHI.2015.2488158
and the initial commercialization of such technologies are the driving force to apply modern sensor systems to aid in the diagnosis, tracking, and treatment of diseases. Successful technology applications for this disease model will be generalizable to a variety of other neurological and musculoskeletal conditions. In contrast to activity monitoring technologies in fitness and health, there are additional hurdles to cross and fundamentally different requirements to meet for applications in medicine and disease. First, for sensor-based assessment, it is necessary to identify the specific impaired motor function before properly quantifying its impairment. Normal quantification of the healthy body function cannot be assumed to be applicable, as the biomechanical quality of the distinct motor function that sensors measure is impaired, and the impairment may even be disease specific. It is also important to note that the biomechanical measurements are only a part of the technology contribution. The mathematical modeling that converts the data into useful information about disease status is at least as important as the development of the measurement devices and must also be validated. Second, there are substantial ethical and legal requirements that underlie the medical applications of technical devices. Unlike a fitness monitor, there are specific requirements for adequate validation of the quantification of distinct motor impairments, especially where it will be used to guide medical decisions. A substantial challenge for medical–technical translational research is the combination of technical expertise with medical needs. On the one hand, fascinating technical developments do not necessarily optimally address the needs of the patient or doctor. On the other hand, many clinicians lack an appreciation of what engineering technologies can do to help them and their patients. Many articles are appearing that use PD as a disease model for the technology development and applications, but duplicative activity measurement studies submitted to technical journals and research grant study sections suggest a lack of awareness of relevant published work and a poor understanding of the disease research gaps. Therefore, the objective of this special issue is to improve and accelerate the productive research in this area, solving clinical needs with enabling technologies. The studies presented here are a representative sample that defines the edge between technical abilities and clinical applications. They demonstrate the usefulness of PD as a model disease opportunity for “convergence” science that will open the door for important technology applications to many other neurological and musculoskeletal movement disorders, as well as defining and improving the healthy patterns of the daily life performance [6]. The papers and their context are briefly summarized here. Using several body-worn inertial motion sensors within a clinical facility, Parisi et al. showed that several distinct items of the UPDRS could be objectively measured even matching
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the clinicians’ rating, but with a reduced interrater variability. An aggregated UPDRS score derived from inertial sensor analysis was proposed for remote monitoring of the PD patients. Impaired gait is one of the most biomechanically characteristic motor symptoms for PD, and is frequently observed and described by specialized physicians during routine diagnostics at the hospital or outpatient units. Wahid et al. presented changes in spatio-temporal gait parameters, assessed by a video motion analysis system and instrumented force platforms in a small patient cohort by deductive calculation and multiple parameter regression analysis. This must be validated, but these data clearly showed that the distinct spatio-temporal gait parameters support the clinical characterization of gait impairment in PD. Demonceau et al. made use of the trunk accelerometer systems to detect similar spatio-temporal gait parameter alterations, but also detected changes in gait regularity and symmetry, adding useful and objective information to the symptoms of shuffling of gait, short steps, and gait asymmetry and instability. In an interventional approach, Jellish et al. showed that treadmill-based systems using real-time feedback to improve distinct gait and posture parameters could increase the step length and posture in PD patients. Another typical symptom representing neuro-muscular dysfunction is the speech impairment in PD, and the clinical presentation differs from other forms of dysphonia. OrozcoArroyave et al. applied a complex analysis of the voice signals to distinguish PD-specific speech impairment from laryngeal pathologies of other origins. Importantly, they showed in a small cohort of patients that different speech impairments require unique algorithmic analysis to distinguish different entities. Translating technical devices for care outside of medical facilities, in the patients’ home environment, is a significant challenge also addressed in this issue. In a longitudinal study, Memedi et al. investigated a telemetry test battery to manage the advanced stage PD, where continuous dopaminergic treatment is achieved by intestinal drug pumps. Constant surveillance of treatment effects and patients’ wellbeing is necessary but challenging in traditional care. This study combined self-rating with instrumented motor tests of upper extremity function; an overall score compared well with distinct motor and nonmotor examinations and, in particular, showed significant changes in patients responding to this therapy. In addition to fine motor skills, tremor can also be a target symptom for instrumented analysis. Kostikis et al. used the inertial sensors from smart phones to detect and classify the tremor in PD patients in a home-based study, also supporting a strategy to keep patients connected with their treating neurologist. Gait impairments are the most frequent and limiting lower extremity dysfunction in PD. In advanced stages of the disease, freezing of gait impairs the patient’s mobility and becomes a major challenge for treatment. Mazilu et al. combined electrocardiography and skin conductance measures in PD patients with an algorithmbased prediction of freezing gait episodes based on defined motor tests, including walking and turning gait sequences. The generated algorithms predicted freezing episodes; this may enable biofeedback-based preemptive therapy approaches. Killiane et al. applied a combination of the virtual reality scenario with a balance board under dual-task conditions to lessen the freezing episodes in the PD patients. They showed that the di-
agnostic tool devices could also be used as an intervention, extending therapeutic strategies. Three more manuscripts reviewed or demonstrated the use of wearable sensors in everyday life scenarios. Stamford et al. reviewed technology applications and patients’ needs that extend beyond motor symptoms, in particular, considering nonmotor symptoms that are so important in daily life of PD patients. Pasluosta et al. summarized how the internet of things is moving into the management of PD patients. Future diagnostic and communication strategies between patients, caregivers, and doctors were discussed, along with projections for wearable technologies shifting treatment and care paradigms. Cook et al. applied these concepts, joining information obtained from smart home and wearable sensors in PD patients and healthy older adults to identify the differences in activity patterns using machine learning classification. In summary, this collection of papers illustrates the wide variety of approaches that can be taken, when leveraging technology to monitor and treat PD. In the wider context of chronic neurologic and musculoskeletal disorders, the contribution in this special issue give rise to new ideas and introduce new technologies with respect to the application of modern sensor systems in aiding diagnosis, tracking, and treatment of these diseases. J. KLUCKEN, Guest Editor Universit¨atsklinikum Erlangen Friedrich-Alexander University Erlangen-N¨urnberg Erlangen 91054, Germany
[email protected] K. E. FRIEDL, Guest Editor University of California San Francisco, CA 94143 USA
[email protected] B. M. ESKOFIER, Guest Editor Friedrich-Alexander University Erlangen-N¨urnberg Erlangen 91058, Germany
[email protected] J. M. HAUSDORFF, Guest Editor Tel Aviv Sourasky Medical Center Tel Aviv 64239, Israel
[email protected] REFERENCES [1] J. Parkinson, An Essay on the Shaking Palsy. London, U.K.: Neely & Jones, 1817. [2] M. M. Hoehn and M. D. Yahr, “Parkinsonism: Onset, progression and mortality,” Neurology, vol. 17, pp. 427–442, 1967. [3] M. Richards, K. Marder, L. Cote, and R. Mayeux, “Interrater reliability of the unified Parkinson’s disease rating scale motor examination,” Movement Disorders, vol. 9, pp. 89–91, 1994. [4] A. Mirelman, N. Giladi, and J.M. Hausdorff, “Body-fixed sensors for Parkinson disease,” J. Amer. Med. Assoc., vol. 314, pp. 873–874, 2015. [5] J. Klucken, J. Barth, P. Kugler, J. Schlachetzki, T. Henze, F. Marxreiter, et al., “Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease,” PloS One, vol. 8, p. e56956, 2013. [6] P. A. Sharp and R. Langer, “Research agenda: Promoting convergence in biomedical science,” Science, vol. 333, p. 527, 2011.
