Adapted step length estimators for patients with Parkinson's disease using a lateral belt worn accelerometer

Abstract

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease that predominantly alters patients' motor performance. Reduced step length and inability of step are important symptoms associated with PD. Assessing patients' motor state monitoring step length helps to detect periods in which patients suffer lack of medication effect. OBJECTIVE: Evaluate the adaption of existing step length estimation methods based on accelerometer sensors to a new position on left lateral side of waist in 28 PD patients. METHODS: In this paper, a user-friendly position, the lateral side of the waist, is selected to place a tri-axial accelerometer. A newly developed step detection algorithm -Sliding Window Averaging Technique (SWAT) is evaluated in detecting steps using signals from this location. The detected steps are then used to estimate step length using four proposed correction factors for Zijlstra's, Gonzalez's and Weinberg's methods that were originally developed for the signals from lower back. RESULT: Results obtained from 28 PD patients are discussed and the effects of calibrating in each motor state are compared. A generic correction factor is also proposed and compared with the best method to use instead of individual calibration. Despite variable gait speed and different motor state, SWAT achieved overall accuracy of 96.76% in step detection. Among the different step length estimators, the Zijlstra method performs better with multiplying individual correction factors that consider left and right step length separately providing average error of 0.033 m. CONCLUSIONS: Zijlstra's method with individual correction factor that considers left and right step length separately and obtained from during ON state of a PD patients provide most accurate estimation among the others. As training session is during ON state, data from induced OFF state to train the methods are not required. A generic correction factor is also proposed to apply with Zijlstra's method to avoid individual calibration process.

The Evaluation of Physical Stillness with Wearable Chest and Arm Accelerometer during Chan Ding Practice

“…Wearable devices are also widely used for health care services [ 9 ]. For medical treatment, phones and wearable systems are well integrated and broadly applied in a variety of motion detections, and sensor networks are used extensively in the detection of daily activities [ 10 , 11 ], such as fall detection [ 12 , 13 ], gait analyses [ 14 ], quality of sleep [ 15 ], energy expenditure [ 16 ], physical therapy exercises [ 17 ], classifying human activities [ 18 ], and even everyday acts in model tracking, such as the actions of people who suffer from Parkinson’s disease [ 19 ]. All these activities have one thing in common: They detect dynamic activities.…”

A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients

“…These algorithms have been previously presented by the authors in other works [3,41,60,61,62,63]. With these classifiers, along with the gait analysis algorithm, an identification of the PD motor state can be performed.…”

“…In addition to the temporal organisation of the algorithms, although it is not interesting to describe each one of the implemented classifiers (Table 5) since they are reported in other papers [3,41,60,61,62,63], it is interesting to describe some specific calculations that are implemented into the microcontroller. Below, the most relevant parts are described, which are the filtering, some temporal and frequency feature extraction and the SVM.…”

“…Then, after asserting that there is a continuous gait cycle, bradykinesia severity is determined. On the other hand, gait parameters are obtained following the method proposed by Sayeed et al [62]. Finally, dyskinesia detection is mainly based on frequency features and the evaluation of different frequency bands in order to estimate its severity [61].…”

The Height-Adaptive Parameterized Step Length Measurement Method and Experiment Based on Motion Parameters

“…As one of the important parameters reflecting people’s motion characteristics, step length can be used in the research of measurements of body motion parameters, disease diagnosis and treatment, health monitoring, rehabilitation training, and pedestrian navigation [ 1 , 2 , 3 , 4 , 5 ]. Motion parameters measured by small Micro Electro Mechanical Systems (MEMS) inertial sensors, at a low cost and with high precision, render step length measurement feasible and effective [ 3 , 6 ].…”