Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world

Hu, Boyi; Li, S.; Chen, Yuhao; Kavi, Rahul; Coppola, Sarah

doi:10.1016/j.apergo.2021.103414

Cited by 25 publications

(27 citation statements)

References 43 publications

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“…To enhance the system's robustness, a promising approach is to delve deeper into the limited sensor measurements and employ neural networks to learn data features and classify the robot's operating states. This methodology has proven effective in various domains, including gait estimation and pose detection, with ample research supporting its efficacy [35][36][37]. A neural network can also be utilized for short-term state estimation with the aim of improving a reference value, as exemplified in the research conducted by Gao et al [38].…”

Section: Introductionmentioning

confidence: 99%

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Liang,

Tao,

Zhu

et al. 2024

Meas. Sci. Technol.

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State estimation is crucial for enabling autonomous mobility in mobile robots. However, traditional localization methods often falter in degraded environments, including issues like visual occlusion, lidar performance degradation, and Global Navigation Satellite System(GNSS) signal interference. This paper presents a novel estimation approach for wheeled robots, exclusively utilizing proprioceptive sensors such as encoders and inertial measurement units (IMU). Initially, the motion manifolds extracted from the historical trajectories are used to assist the encoder in realizing the orientation estimation. Furthermore, a hybrid neural network is designed to categorize the robot's operational state, and the corresponding pseudo-constraints are added to improve the estimation accuracy. We utilize an error state Kalman filter for the encoder and IMU data fusion. Lastly, comprehensive testing is conducted using both datasets and real-world robotic platforms. The findings underscore that the integration of manifold and motion constraints within our proposed state estimator substantially elevates accuracy compared to conventional approaches. Compare with the methods commonly used in engineering, the accuracy of this method is improved by more than 20%. Crucially, this methodology enables dependable estimation even in degraded environments.

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Section: Introductionmentioning

confidence: 99%

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Liang,

Tao,

Zhu

et al. 2024

Meas. Sci. Technol.

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“…DNN achieved the best recognition accuracy. Hu et al [24] used six IMU installed on the body to train three deep-learning models, all of which achieved more than 90% accuracy. Te experimental results further proved that the use of deep learning models and wearable IMU sensors had great potential in gait analysis.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on a Modified Capsule Network

Zhu

Chen

Liu

et al. 2023

Mobile Information Systems

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Human activity recognition (HAR) has attracted considerable research attention in the past decade with the development of wearable sensor technology and deep learning algorithms. However, most of the existing HAR methods ignored the spatial relationship of features, which may lead to recognition errors. In this paper, a novel model based on a modified capsule network (MCN) is proposed to accurately recognize various human activities. This novel model is composed of a convolution block and a capsule block, which can achieve end-to-end intelligent recognition. In the meantime, the spatial information among features is preserved through a dynamic routing process. To validate the effectiveness of the model, a human activity dataset is constructed by placing an inertial measurement unit (IMU) on the calf of the volunteers to collect their activity data in daily life, including walking, jogging, upstairs, downstairs, up-ramps, and down-ramps. The recognition accuracy of this novel approach can reach 96.08%, which performs better than the convolutional neural network (CNN) with an accuracy of 91.62%. In addition, it is evaluated on two public datasets named WISDM and UCI-HAR, and the accuracies achieve 98.21% and 95.28%, respectively, which presents higher accuracy than the reported results obtained from benchmark algorithms like CNN. The experimental results show that the proposed model has better activity detection capability and achieves outstanding performance for HAR.

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“…A wrist-mounted voice recorder was previously utilized to capture contextual information following misstep events (trips) [ 16 ], which could be limited to observations made by the user and may lack spatial and temporal resolution. To objectively identify terrain types, several studies examined the feasibility of using wearable IMU data recorded during gait [ 17 – 19 ]. For instance, machine learning models achieved 89 accuracy (10-fold cross-validation) to detect six different terrains including soil and concrete using two IMUs in [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Egocentric vision-based detection of surfaces: towards context-aware free-living digital biomarkers for gait and fall risk assessment

Nouredanesh

Godfrey

Powell

et al. 2022

J NeuroEngineering Rehabil

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Background Falls in older adults are a critical public health problem. As a means to assess fall risks, free-living digital biomarkers (FLDBs), including spatiotemporal gait measures, drawn from wearable inertial measurement unit (IMU) data have been investigated to identify those at high risk. Although gait-related FLDBs can be impacted by intrinsic (e.g., gait impairment) and/or environmental (e.g., walking surfaces) factors, their respective impacts have not been differentiated by the majority of free-living fall risk assessment methods. This may lead to the ambiguous interpretation of the subsequent FLDBs, and therefore, less precise intervention strategies to prevent falls. Methods With the aim of improving the interpretability of gait-related FLDBs and investigating the impact of environment on older adults’ gait, a vision-based framework was proposed to automatically detect the most common level walking surfaces. Using a belt-mounted camera and IMUs worn by fallers and non-fallers (mean age 73.6 yrs), a unique dataset (i.e., Multimodal Ambulatory Gait and Fall Risk Assessment in the Wild (MAGFRA-W)) was acquired. The frames and image patches attributed to nine participants’ gait were annotated: (a) outdoor terrains: pavement (asphalt, cement, outdoor bricks/tiles), gravel, grass/foliage, soil, snow/slush; and (b) indoor terrains: high-friction materials (e.g., carpet, laminated floor), wood, and tiles. A series of ConvNets were developed: EgoPlaceNet categorizes frames into indoor and outdoor; and EgoTerrainNet (with outdoor and indoor versions) detects the enclosed terrain type in patches. To improve the framework’s generalizability, an independent training dataset with 9,424 samples was curated from different databases including GTOS and MINC-2500, and used for pretrained models’ (e.g., MobileNetV2) fine-tuning. Results EgoPlaceNet detected outdoor and indoor scenes in MAGFRA-W with 97.36$$\%$$ % and 95.59$$\%$$ % (leave-one-subject-out) accuracies, respectively. EgoTerrainNet-Indoor and -Outdoor achieved high detection accuracies for pavement (87.63$$\%$$ % ), foliage (91.24$$\%$$ % ), gravel (95.12$$\%$$ % ), and high-friction materials (95.02$$\%$$ % ), which indicate the models’ high generalizabiliy. Conclusions Encouraging results suggest that the integration of wearable cameras and deep learning approaches can provide objective contextual information in an automated manner, towards context-aware FLDBs for gait and fall risk assessment in the wild.

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Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world

Cited by 25 publications

References 43 publications

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Human Activity Recognition Based on a Modified Capsule Network

Egocentric vision-based detection of surfaces: towards context-aware free-living digital biomarkers for gait and fall risk assessment

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