Freezing of Gait detection in Parkinson's disease using accelerometer based smart clothes

Abstract

Parkinson's disease (PD) is a degenerative neurological disorder. Patients suffer from different movement disturbances. Amongst others, freezing of gait (FOG) is a common and disabling feature. A new method for FOG detection using a garment with integrated accelerometers, the MiMed-Pants, is presented and evaluated. MiMed-Pants are loose fitting washable pants for measuring movements and were previously developed. The accelerations of shanks and belt of 6 patients were recorded while walking on two standardized courses. The occurrence of FOG were detected and documented both by the new method and by a physician. Those recordings were then compared afterwards to determine the new method's sensitivity and specificity. With this wearable measurement device FOG could be detected without the distracting effects of an extensive measurement installation.

Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

“…The results previously obtained by Niazmand et al [23], Cole et al [7] and Bächlin et al [5] were consistently outperformed by the presented approach in its third variation, which has yielded to an average sensitivity and specificity above 94%. Niazmand et al [23] achieved a sensitivity of 88.3% and a specificity of 85.3%.…”

“…Niazmand et al [23] achieved a sensitivity of 88.3% and a specificity of 85.3%. Compared to the results from Bächlin et al [5], both sensitivity Table 5: Results in detecting FOG with the two-sided approach (i.e.…”

“…Moreover, we only use a single tri-axial accelerometer at the waist (e.g. Cole et al [7]: three tri-axial accelerometers and surface EMG, Niazmand et al [23]: five accelerometers, Bächlin et al [5]: three accelerometers and three gyroscopes). The presented approach offers configuration capabilities since the algorithm can be tuned toward high sensitivity and high specificity by adjusting the thresholds.…”

“…The approach by Cole et al [7] yielded to 82.9% sensitivity and 97.3% specificity in detecting FOG (using acceleration and electromyograph (EMG) sensors) with a multi-staged algorithm that utilized a simple linear classifier and a dynamic neural network (DNN). Niazmand et al [23] used an accelerometer based smart garment [22] to extract gait-related features. They achieved 88.3% sensitivity and 85.3% specificity (using multiple accelerometers).…”

A holistic approach to assess the feasability of telehealth for Parkinson's disease management

“…6 patients were recorded while walking on two standardized courses. FoG events were igually identified and recorded both by the online approach and by a physician [435], [436]. Finally, worked also in a wireless smart glove for the evaluation of severity of motor dysfunction in PD.…”