Simultaneous Bayesian Recognition of Locomotion and Gait Phases With Wearable Sensors

Martinez-Hernandez, Uriel; Mahmood, Imran; Dehghani-Sanij, Abbas A.

doi:10.1109/jsen.2017.2782181

Cited by 55 publications

(35 citation statements)

References 36 publications

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“…The participants were instructed to walk a forty-meter flat ground four times, and then walked back and forth twenty times on a slope with a length of 5 m angled at 5.2 degrees. Similar ramp angle degrees are also found in other relevant studies and they are summarized in Table 2 for comparison with our work [19,22,27,31,35]. Table 2.…”

Section: Experiments Setupsupporting

confidence: 88%

“…In [19], a terrain identification-enabled exoskeleton system which integrated only two attitude and heading reference system (AHRS) sensors and three GRF sensors was designed, tested by three healthy subjects and successfully identified ramp and level ground, with a mean accuracy of 97.77%. The number and categories of the applied sensors and the classification accuracy of the above studies, along with some other relevant studies [17,[33][34][35], are summarized in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

et al. 2020

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Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular–mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal–Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation.

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Section: Experiments Setupsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

et al. 2020

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“…Yuwono et al [18] presented a single IMU system that identifies bilateral heel-strike events with the use of the Bayesian method. Martinez-Hernandez et al [11] proposed a Bayesian formulation to achieve high recognition accuracy of simultaneous daily activities and gait phases recognition with a small number of sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Results showed that the closed-loop approach would facilitate the adaptation from patient to patient. A multilayer architecture is recognised to be essential for intelligent systems to perform robust data processing, perception, and action at different levels of abstraction [11]. Our work could be extended to include the high and low-level process of robotic control in real-time.…”

Section: Accuracy Of Ankle Angle Measurementmentioning

confidence: 99%

“…The IMUs have been popularly adopted in ambulatory systems due to their small size, low cost, and low power consumption. The use of either whole IMU consisting of gyroscopes, accelerometers, and magnetometers or parts of it to determine kinematic data [10], activity recognition [11] or gait event classification [12], [13] has shown promising results.…”

Section: Introductionmentioning

confidence: 99%

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A Practical Gait Feedback Method Based on Wearable Inertial Sensors for a Drop Foot Assistance Device

et al. 2019

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To maximise the efficiency of gait interventions, gait phase and joint kinematics are important for closing the system loop of adaptive robotic control. However, few studies have applied an inertial sensor system including both gait phase detection and joint kinematic measurement. Many algorithms for joint measurement require careful alignment of the inertial measurement unit (IMU) to the body segment. In this paper, we propose a practical gait feedback method, which provides sufficient feedback without requiring precise alignment of the IMUs. The method incorporates a two-layer model to realise simultaneous gait stance and swing phase detection and ankle joint angle measurement. Recognition of gait phases is performed by a high-level probabilistic method using angular rate from the sensor attached to the shank while the ankle angle is calculated using a data fusion algorithm based on the complementary filter and sensor-to-segment calibration. The online performance of the algorithm was experimentally validated when 10 able-bodied participants walked on the treadmill with three different speeds. The outputs were compared to the ones measured by an optical motion analysis system. The results showed that the IMU-based algorithm achieved a good accuracy of the gait phase recognition (above 95%) with a short delay response below 20 ms and accurate angle measurements with root mean square errors below 3.5 • compared to the optical reference. It demonstrates that our method can be used to provide gait feedback for the correction of drop foot.

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Assistive Gait Wearable Robots—From the Laboratory to the Real Environment

Abouhossein

Martinez-Hernandez

Awad

et al. 2020

Reinventing Mechatronics

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Simultaneous Bayesian Recognition of Locomotion and Gait Phases With Wearable Sensors

Abstract: /0000-0002-9922-7912, Mahmood, I and Dehghani-Sanij, AA (2018) Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors.

Cited by 55 publications

References 36 publications

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

A Practical Gait Feedback Method Based on Wearable Inertial Sensors for a Drop Foot Assistance Device

Assistive Gait Wearable Robots—From the Laboratory to the Real Environment

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