Clinicians need a practical, objective test of postural control that is sensitive to mild neurological disease, shows experimental and clinical validity, and has good test-retest reliability. We developed an instrumented test of postural sway (ISway) using a body-worn accelerometer to offer an objective and practical measure of postural control.

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

Objective: To quantify postural sway in subjects with Parkinson's disease and elderly controls, and determine the effects of Parkinson's disease, deep brain stimulation, levodopa, and their interactions on postural control during quiet stance. Methods: Centre of foot pressure (CoP) displacement under each foot was measured during three 60 s trials of quiet stance with eyes open in 11 controls and six patients with Parkinson's disease. Subjects with Parkinson's disease were tested in four treatment conditions: off both deep brain stimulation and levodopa (off condition); on deep brain stimulation; on levodopa; and on both deep brain stimulation and levodopa. The variables extracted from CoP included: root mean square distance (rms), mean velocity, 95% power frequency (f_95%), area of the 95% confidence ellipse (ellipse area), direction of its major axis (mdir), and postural asymmetry between the feet. Results: rms and area of postural sway were larger than normal in subjects with Parkinson's disease in the off condition, increased further with levodopa, and significantly decreased with deep brain stimulation. Mean velocity and f_95% were also larger than normal but were restored to normal by all treatments, especially by deep brain stimulation. The combined effect of deep brain stimulation and levodopa resulted in a postural sway that was an average of the effect of each treatment individually. Levodopa increased sway more in the mediolateral than in the anterior-posterior direction. Subjects with Parkinson's disease had asymmetrical mean velocity and f_95% between the feet, and this asymmetry increased with levodopa but decreased with deep brain stimulation. The f_95% of the CoP correlated with tremor, posture, and gait subcomponents of the unified Parkinson's disease rating scale. Conclusions: Subjects with Parkinson's disease have abnormal postural sway in stance. Treatment with levodopa increases postural sway abnormalities, whereas treatment with deep brain stimulation improves postural sway. Quantitative evaluation of static posturography may be a useful adjunct to clinical measures in patients with Parkinson's disease.