Fractal dynamics were recently detected in the apparently "noisy" variations in the stride interval of human walking. Dynamical analysis of these step-to-step fluctuations revealed a self-similar pattern: fluctuations at one time scale are statistically similar to those at multiple other time scales, at least over hundreds of steps, while healthy subjects walk at their normal rate. To study the stability of this fractal property, we analyzed data obtained from healthy subjects who walked for 1 h at their usual, slow, and fast paces. The stride interval fluctuations exhibited long-range correlations with power-law decay for up to 1,000 strides at all 3 walking rates. In contrast, during metronomically paced walking, these long-range correlations disappeared; variations in the stride interval were random (uncorrelated) and nonfractal. The long-range correlations observed during spontaneous walking were not affected by removal of drifts in the time series. Thus the fractal dynamics of spontaneous stride interval are normally quite robust and intrinsic to the locomotor system. Furthermore, this fractal property of neural output may be related to the higher nervous centers responsible for the control of walking rhythm.

In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capturing of more and previously inaccessible phenomena in Parkinson disease (PD). However, more information has not translated into greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include non-compatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (in particular among vulnerable elderly patients), and the gap between the “big data” acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms enabling multi-channel data capture, sensitive to the broad range of motor and non-motor problems that characterize PD, and adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to: 1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones; 2) enhance tailoring of symptomatic therapy; 3) improve subgroup targeting of patients for future testing of disease modifying treatments; and 4) identify objective biomarkers to improve longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the Task Force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and quality of life of individuals with PD.