Can Trunk Acceleration Differentiate Stroke Patient Gait Patterns Using Time- and Frequency-Domain Features?

Hsu, Wei‐Chun; Sugiarto, Tommy; Liao, Ying-Yi; Lin, Yi‐Ying; Yang, Fu‐Chi; Hueng, Dueng‐Yuan; Sun, Chi-Tien; Chou, Kuan‐Nien

doi:10.3390/app11041541

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Trunk acceleration measurement by IMU during gait represents the characteristics of walking and is useful to identify gait abnormalities [ 7 ]. IMUs can be used not only for calculating important gait parameters, such as spatiotemporal parameters [ 8 – 10 ], or for investigating the gait stability and variability between normal and pathological gaits but also for classifying different types of gait patterns, for example, the stroke patient gait pattern [ 11 ] or Parkinson's disease gait pattern [ 12 ]. These benefits have led to gait analysis with IMUs being widely used in clinical settings and care units.…”

Section: Introductionmentioning

confidence: 99%

Immediate Effect of Restricted Knee Extension on Ground Reaction Force and Trunk Acceleration during Walking

Osaka

Fujita

Kobara

et al. 2021

Rehabilitation Research and Practice

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Gait parameters calculated from trunk acceleration reflect the features of gait; however, they cannot evaluate the gait pattern corresponding to the gait cycle. This study is aimed at investigating the differences in gait parameters calculated from trunk acceleration during gait corresponding to the gait cycle in healthy subjects with restricted knee extension. Participants included eight healthy volunteers who walked normally (NW) and with knee orthosis that restricted knee extension (ER). The ground reaction force (GRF), joint angles, and trunk acceleration during walking were measured using four force plates, a three-dimensional motion analysis system, and an inertial measurement unit. The peak GRF of the vertical components, joint ranges of motion, and moments of force were analyzed. The root mean square (RMS) and amplitude peak ratio (AR) of autocorrelation function were calculated from the trunk acceleration waveform. The first peak GRF and peak ankle dorsiflexion angles significantly increased during ER. The peak hip extension, knee flexion, knee extension angles, and the peak moment of knee extension significantly decreased during ER compared to that during NW. The acceleration AR significantly decreased during ER compared to that during NW. There was no significant difference in the RMS between the two conditions. The acceleration AR may show the temporal postural structure with restricted knee extension from the terminal stance phase for the ipsilateral limb to the initial stance phase for the contralateral limb. These results suggest that novel metrics for accelerometry gait analysis can reveal gait abnormalities, with restricted knee extension corresponding to the gait cycle.

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Section: Introductionmentioning

confidence: 99%

Immediate Effect of Restricted Knee Extension on Ground Reaction Force and Trunk Acceleration during Walking

Osaka

Fujita

Kobara

et al. 2021

Rehabilitation Research and Practice

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“…Subsequently, these traits were combined and employed to train a classifier with the objective of achieving recognition. Hsu et al [41] examined the patients' movement patterns by computing the bandwidth frequency, skewness and kurtosis features using time series sensory data. The effectiveness of HFT greatly depends on the researchers' expertise in the desired domain and their capacity to record significant information from the unprocessed data [42,43].…”

Section: Handcrafted Feature-based Techniquesmentioning

confidence: 99%

“…Amjad et al [39] atomic sp, sw, sg Handcrafted Sargano et al [40] atomic videos Handcrafted Hsu et al [41] atomic IMU sensor Handcrafted Lagodzinski et al [45] composite sg Codebook Nisar et al [34] atomic, composite sp, sw, sg Codebook Koping et al [47] composite sp, sw, sg Codebook Zhang et al [50] atomic, composite wifi devices LSTM Bianchi et al [51] composite IMUs CNN Anagnostis et al [52] atomic IMUs LSTM Bu et al [53] atomic IMUs CNN Huang et al [55] atomic IMUs CNN Kolkar et al [57] composite IMUs CNN + GRU Dua et al [58] atomic IMUs CNN + GRU Khatun et al [59] atomic IMUs CNN + LSTM Nisar et al [61] atomic, composite sp, sw, sg CNN + LSTM…”

Section: Ref Activity Type Modality Type Featuresmentioning

confidence: 99%

Identification of Optimal Data Augmentation Techniques for Multimodal Time-Series Sensory Data: A Framework

Ashfaq,

Khan,

Nisar

2024

Information

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Recently, the research community has shown significant interest in the continuous temporal data obtained from motion sensors in wearable devices. These data are useful for classifying and analysing different human activities in many application areas such as healthcare, sports and surveillance. The literature has presented a multitude of deep learning models that aim to derive a suitable feature representation from temporal sensory input. However, the presence of a substantial quantity of annotated training data is crucial to adequately train the deep networks. Nevertheless, the data originating from the wearable devices are vast but ineffective due to a lack of labels which hinders our ability to train the models with optimal efficiency. This phenomenon leads to the model experiencing overfitting. The contribution of the proposed research is twofold: firstly, it involves a systematic evaluation of fifteen different augmentation strategies to solve the inadequacy problem of labeled data which plays a critical role in the classification tasks. Secondly, it introduces an automatic feature-learning technique proposing a Multi-Branch Hybrid Conv-LSTM network to classify human activities of daily living using multimodal data of different wearable smart devices. The objective of this study is to introduce an ensemble deep model that effectively captures intricate patterns and interdependencies within temporal data. The term “ensemble model” pertains to fusion of distinct deep models, with the objective of leveraging their own strengths and capabilities to develop a solution that is more robust and efficient. A comprehensive assessment of ensemble models is conducted using data-augmentation techniques on two prominent benchmark datasets: CogAge and UniMiB-SHAR. The proposed network employs a range of data-augmentation methods to improve the accuracy of atomic and composite activities. This results in a 5% increase in accuracy for composite activities and a 30% increase for atomic activities.

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“…The authors employed the location and accelerometer sensory data of a smartphone to extract nine gait features, including coefficient of variance, step count, cadence, regularity in step, stride, etc. Similarly, the authors of [ 32 ] extracted standard deviation, skewness, kurtosis, and bandwidth frequency features from the accelerometer data of an IMU sensor mounted on the subject’s lower back to distinguish between normal and stroke gait patterns. The study presented in [ 33 ] extracted thirty-eight statistical quantities, including maximum, minimum, average, spectral energy, etc., to monitor and quantify various human physical activities using a smartphone’s IMU sensory data.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

Fatima,

Khan,

Nisar

et al. 2023

Sensors

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This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

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Can Trunk Acceleration Differentiate Stroke Patient Gait Patterns Using Time- and Frequency-Domain Features?

Cited by 5 publications

References 38 publications

Immediate Effect of Restricted Knee Extension on Ground Reaction Force and Trunk Acceleration during Walking

Immediate Effect of Restricted Knee Extension on Ground Reaction Force and Trunk Acceleration during Walking

Identification of Optimal Data Augmentation Techniques for Multimodal Time-Series Sensory Data: A Framework

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

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