Reliable and Robust Detection of Freezing of Gait Episodes With Wearable Electronic Devices

Abstract

A wearable wireless sensing system for assisting 1 patients affected by Parkinson's disease is proposed. It uses inte-2 grated micro-electro-mechanical inertial sensors able to recognize 3 the episodes of involuntary gait freezing. The system operates in 4 real time and is designed for outdoor and indoor applications. 5 Standard tests were performed on a noticeable number of 6 patients and healthy persons and the algorithm demonstrated 7 its reliability and robustness respect to individual specific gait 8 and postural behaviors. The overall performances of the system 9 are excellent with a specificity higher than 97%.

l-DOPA and Freezing of Gait in Parkinson’s Disease: Objective Assessment through a Wearable Wireless System

“…The time domain analysis has the great advantage of performing a lower number of calculations, which turns into smaller power consumption and longer battery life. So far, very few studies with the pure time domain approach have been reported and among them, the work by Kwon et al ( 25 ), which was based on the use of the root mean square of the accelerometer signal, and our previous work ( 52 ), which was based on the fusion of raw accelerometers and gyroscope signals. Both time domain approaches detected FOG episodes through a threshold method ( 25 , 52 ).…”

“…So far, very few studies with the pure time domain approach have been reported and among them, the work by Kwon et al ( 25 ), which was based on the use of the root mean square of the accelerometer signal, and our previous work ( 52 ), which was based on the fusion of raw accelerometers and gyroscope signals. Both time domain approaches detected FOG episodes through a threshold method ( 25 , 52 ). Kwon et al ( 25 ) studied 20 patients with PD, obtaining a SE and a SP over 85%, whereas Kita et al ( 52 ) studied 16 patients with PD, obtaining a SE and a SP over 94%.…”

“…The core of the wearable wireless system used for FOG detection and gait analysis consisted of two Inertial Measurements Units (IMUs) placed on the shins (Figure 1 ) ( 51 , 52 ). The prototype IMU board was designed for processing signals in real time.…”

“…A quaternion-based representation of the limbs orientation and position was calculated, and a 3D vector representing the limbs was generated. The sampling frequency ( f s ) was 60 Hz ( 51 ) using a PC for the postprocessing, while it can be better set at 25 Hz ( 52 ) using a smartphone as a portable receiver. Reducing the sampling frequency has a benefit in that the number of transmitted data and operations per unit time becomes lower, thus improving the sensors and smartphone battery life.…”

Embedded Wearable Integrating Real-Time Processing of Electromyography Signals

“…In this frame, wearables devices and body sensor networks are attracting most attention for the treatment of chronic diseases of elderly, who need monitoring of the symptoms and adjustment of pharmacological therapies [1,2].…”