Instrumental Assessment of Stair Ascent in People With Multiple Sclerosis, Stroke, and Parkinson’s Disease: A Wearable-Sensor-Based Approach

Carpinella, Ilaria; Gervasoni, Elisa; Anastasi, Denise; Lencioni, Tiziana; Cattaneo, Davide; Ferrarin, Maurizio

doi:10.1109/tnsre.2018.2881324

Cited by 24 publications

(28 citation statements)

References 52 publications

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“…In particular, wearable inertial measurement units (IMUs), including accelerometers, gyroscopes, and magnetometers, represent cost-effective tools to perform objective assessments of walking in pwMS outside movement analysis labs [19,20], and even during free-living and community contexts [21,22]. IMUs have been widely used to analyse different locomotor tasks in pwMS, such as straight-line over ground [17,[23][24][25][26][27] and treadmill walking [28], standing up, walking, turning, and sitting down (e.g., the TUG) [15,29], walking with head turns and over/around obstacles [30,31], walking while texting [32], and stairway walking [33]. During these tests, several parameters have been extracted from IMUs, including spatio-temporal parameters [15,24,27,28,31,32,34], indexes of gait variability and stability [17,23,24,26,31,33], trunk sway metrics [15,23,30,34], and angular variables [15,25,27,34].…”

Section: Introductionmentioning

confidence: 99%

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Angelini

Carpinella

Cattaneo

et al. 2019

Sensors

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Inertial measurement units (IMUs) allow accurate quantification of gait impairment of people with multiple sclerosis (pwMS). Nonetheless, it is not clear how IMU-based metrics might be influenced by pragmatic aspects associated with clinical translation of this approach, such as data collection settings and gait protocols. In this study, we hypothesised that these aspects do not significantly alter those characteristics of gait that are more related to quality and energetic efficiency and are quantifiable via acceleration related metrics, such as intensity, smoothness, stability, symmetry, and regularity. To test this hypothesis, we compared 33 IMU-based metrics extracted from data, retrospectively collected by two independent centres on two matched cohorts of pwMS. As a worst-case scenario, a walking test was performed in the two centres at a different speed along corridors of different lengths, using different IMU systems, which were also positioned differently. The results showed that the majority of the temporal metrics (9 out of 12) exhibited significant between-centre differences. Conversely, the between-centre differences in the gait quality metrics were small and comparable to those associated with a test-retest analysis under equivalent conditions. Therefore, the gait quality metrics are promising candidates for reliable multi-centric studies aiming at assessing rehabilitation interventions within a routine clinical context.

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Section: Introductionmentioning

confidence: 99%

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Angelini

Carpinella

Cattaneo

et al. 2019

Sensors

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“…In the recent literature there is evidence that PD individuals are at higher risk of falls when descending stairs (7) because of a reduced ability to produce adequate muscle strength, in particular reduced strength for the knee extensors (6). Moreover, a recent study investigated transitional tasks from quiet standing to step climbing (21,45). The authors noticed reduced step frequency and a significant reduction of the medio-lateral acceleration in PD individuals undergoing a step climbing task compared to level ground walking.…”

Section: Discussionmentioning

confidence: 99%

Transitional Locomotor Tasks in People With Mild to Moderate Parkinson's Disease

Kamieniarz¹,

Michalska²,

Marszałek³

et al. 2020

Front. Neurol.

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Background: People with Parkinson's disease (PD) exhibit deficits in maintaining balance both during quiet standing and during walking, turning, standing up from sitting, and step initiation. Objective: The purpose of this study was to examine balance disorders during a transitional task under different conditions in participants with PD. Methods: The research was conducted on 15 PD-II (mild) and 15 PD-III (moderate) individuals (H&Y II-III stage) and 30 healthy elderly. The transitional task was measured on two force platforms (A and B). The procedure consisted of three phases: (1) quiet standing on platform A, (2) crossing to platform B, and (3) quiet standing on platform B, each until measurements were completed. There were four conditions: crossing without an obstacle, crossing with an obstacle, and walking up and down the step. Results: There were no significant differences between mild PD individuals and healthy elderly during quiet standing before the transitional task and after completing the task. The temporal aspects describing the different transitional tasks were comparable between mild PD and healthy subjects. Moderate PD participants presented a significantly higher COP velocity after the transitional task compared to the healthy older adults (p < 0.05). Additionally, the moderate PD group showed significantly higher values for transit time relative to healthy subjects during the transitional task in all conditions (p < 0.05). Conclusions: Disease severity affects the temporal aspects of different transitional tasks in people with PD. The procedure of completing a transitional task under different conditions allowed differences between moderate and mild PD stages and healthy subjects to be observed.

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“…In the context of HAR for detection of stair climbing, approaches mainly differ for the sensing modality, position of the sensor/s, features, algorithms used for classification, and target users. Sensors comprise IMUs [5,[12][13][14][15] and their combined use with barometers [16][17][18][19][20][21]. Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21].…”

Section: Introductionmentioning

confidence: 99%

“…Sensors comprise IMUs [5,[12][13][14][15] and their combined use with barometers [16][17][18][19][20][21]. Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21]. Common features are averages, ranges, Fourier Transform and Wavelet coefficients, statistical moments of sensor data, whereas classifiers are Support Vector Machine (SVM) [5,12,16,17,19], K-Nearest Neighbour (KNN) [5,16,17], Decision Tree and Random Forest [5,13,17,19], Artificial Neural Network (ANN) [22], and also convolutional NN [15].…”

Section: Introductionmentioning

confidence: 99%

“…Common features are averages, ranges, Fourier Transform and Wavelet coefficients, statistical moments of sensor data, whereas classifiers are Support Vector Machine (SVM) [5,12,16,17,19], K-Nearest Neighbour (KNN) [5,16,17], Decision Tree and Random Forest [5,13,17,19], Artificial Neural Network (ANN) [22], and also convolutional NN [15]. Users span from healthy young people up to elderly patients with severe conditions such as Parkinson's disease or stroke [5,16,20].…”

Section: Introductionmentioning

confidence: 99%

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Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

et al. 2020

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Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB.

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Instrumental Assessment of Stair Ascent in People With Multiple Sclerosis, Stroke, and Parkinson’s Disease: A Wearable-Sensor-Based Approach

Cited by 24 publications

References 52 publications

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Transitional Locomotor Tasks in People With Mild to Moderate Parkinson's Disease

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

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