eZiGait: Toward an AI Gait Analysis And Sssistant System

McCalmont, Graham; Morrow, Philip; Zheng, Huiru; Samara, Anas; Yasaei, Sara; Wang, Haiying; McClean, Sally

doi:10.1109/bibm.2018.8621176

Cited by 5 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This research achieved 99.8% accuracy in recognizing these activities. Forest classifiers [35]. In this study, the ANN classifier was able to achieve the highest accuracy of 80% with both the KNN and random forest achieving an accuracy of 70% in the recognition of slow, normal, and fast walking, stair ascent, and stair descent activities.…”

Section: Feature Extractionmentioning

confidence: 68%

“…4b, Lopez-Nava et al proposed the use of one IMU placed on the right ankle, to collect data for the detection of gait events, such as toe-off and heel-strike, and for the recognition of activities, such as level-ground walking, stairs ascent, stairs descent, ramp ascent, and ramp descent [29]. Similarly, the use of a single IMU worn on the ankle to collect data for the recognition of walking, stair ascent, and stairs descent activities was presented by McCalmont et al [35]. As shown in Fig.…”

Section: Wearable Sensorsmentioning

confidence: 99%

“…To address this problem, most WGA-based activity recognition systems use gait cycles to segment sensor data [14,23,35,42]. One full gait cycle begins with one repetitive gait event (e.g., heel-strike or toeoff) and continues until the occurrence of the same gait event on the same foot.…”

Section: Table 1 Continuedmentioning

confidence: 99%

“…Examples of time-domain features include mean value, variance, median, skewness, percentile, and interquartile ranges, etc. McCalmont et al [35] used 30 features consisting of the time domain features such as the mean and standard deviation of the acceleration, and angular velocity signals in the recognition of slow walking, normal walking, fast walking, stair ascent, and stair descent activities. Frequency-domain features include the signal power, spectral entropy, auto-correlation coefficients, mean frequency, median frequency, etc.…”

Section: Feature Extractionmentioning

confidence: 99%

See 3 more Smart Citations

Wearable-Gait-Analysis-Based Activity Recognition: A Review

Ansah

Chen

2022

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Gait analysis has been recognized as an efficient method to help realize human activity recognition; however, there is currently no existing review study focused on wearable activity recognition methods that employ gait analysis in the recognition process. In this study, different wearable-gait-analysis-based (WGA-based) activity recognition methods were summarized and compared from the aspects of wearable sensor types, data segmentation, feature extraction, and classification methods. The limitations of the current research and potential opportunities for future research in this field are also discussed.

show abstract

Section: Feature Extractionmentioning

confidence: 68%

Section: Wearable Sensorsmentioning

confidence: 99%

Section: Table 1 Continuedmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Wearable-Gait-Analysis-Based Activity Recognition: A Review

Ansah

Chen

2022

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…McCalmont et al [20] developed a framework for analysing and assessing human gait using smart insoles. The smart insole was composed of eight pressure sensors and nine degrees IMU sensors (accelerometer, gyroscope, and magnetometer).…”

Section: Related Workmentioning

confidence: 99%

Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition

D'Arco

Wang

Zheng

2022

MPs

Self Cite

View full text Add to dashboard Cite

Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection technique has been applied to reduce the number of features from 272 to 227 to improve the robustness of the proposed system and to investigate the importance of features in the dataset. According to the findings, the inertial sensors are reliable for the recognition of dynamic activities, while pressure sensors are reliable for stationary activities; however, the highest accuracy (94.66%) was achieved by combining both types of sensors.

show abstract