Gait Phase Detection Based on a Foot-Mounted Inertial Sensor Using Long Short-Term Memory Enhanced by Hidden Markov Model

Modern programming relies on a large number of fundamental APIs, but programmers often take great effort to remember names and the usage of APIs when coding, and repeatedly search the related API documents or Q&A websites (e.g. Stack Overflow). To improve the programming efficiency, we present a Java API suggestion model called APIHelper which learns API sequence pattern via the Long Short-Term Memory (LSTM) network, then provides API suggestion based on the program context. Comparing with statistical methods (e.g. Hidden Markov Model (HMM), N-gram), which require establishing one specific model for each class, we propose Deterministic Negative Sampling (DNS) to make API suggestion for a large number of Java classes by one single end-to-end LSTM. To verify this approach, we make API suggestion for 50,000 Java classes and evaluate it with accuracy and top-K accuracy. The results show that APIHelper outperforms other research works both on accuracy and computation efficiency.

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Deep stair walking detection using wearable inertial sensor via long short-term memory network

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i1.1685 ◽

2020 ◽

Vol 9 (1) ◽

pp. 238-246

Author(s):

Gan Wei Nie ◽

Nurul Fathiah Ghazali ◽

Norazman Shahar ◽

Muhammad Amir As'ari

Keyword(s):

Short Term Memory ◽

Inertial Sensors ◽

Inertial Sensor ◽

Window Size ◽

Sensor Data ◽

Short Term ◽

Term Memory ◽

Detection Model ◽

Long Short Term Memory ◽

Stair Walking

This paper proposes a stair walking detection via Long-short Term Memory (LSTM) network to prevent stair fall event happen by alerting caregiver for assistance as soon as possible. The tri-axial accelerometer and gyroscope data of five activities of daily living (ADLs) including stair walking is collected from 20 subjects with wearable inertial sensors on the left heel, right heel, chest, left wrist and right wrist. Several parameters which are window size, sensor deployment, number of hidden cell unit and LSTM architecture were varied in finding an optimized LSTM model for stair walking detection. As the result, the best model in detecting stair walking event that achieve 95.6% testing accuracy is double layered LSTM with 250 hidden cell units that is fed with data from all sensor locations with window size of 2 seconds. The result also shows that with similar detection model but fed with single sensor data, the model can achieve very good performance which is above 83.2%. It should be possible, therefore, to integrate the proposed detection model for fall prevention especially among patients or elderly in helping to alert the caregiver when stair walking event occur.

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Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis—Proof of Concept

Sensors ◽

10.3390/s21175749 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5749

Author(s):

Mustafa Sarshar ◽

Sasanka Polturi ◽

Lutz Schega

Keyword(s):

Gait Analysis ◽

Short Term Memory ◽

Inertial Sensors ◽

Validation Dataset ◽

Phase Detection ◽

Short Term ◽

Velocity Magnitude ◽

Gait Phase ◽

Long Short Term Memory ◽

Do So

Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. The aim of this work was to employ Artificial Intelligence (AI), specifically a long short-term memory (LSTM) algorithm, to overcome these weaknesses. Three supervised LSTM-based models were designed to estimate the expected gait phases, including foot-off (FO), mid-swing (MidS) and foot-contact (FC). For collecting gait data two tri-axial inertial sensors were located above each ankle. The angular velocity magnitude, rotation matrix magnitude and free acceleration magnitude were captured for data labeling and turning detection and to strengthen the model, respectively. To do so, a train dataset based on a novel movement protocol was acquired. A validation dataset similar to a train dataset was generated as well. Five test datasets from already existing data were also created to independently evaluate the models. After testing the models on validation and test datasets, all three models demonstrated promising performance in estimating desired gait phases. The proposed approach proves the possibility of employing AI-based algorithms to predict labeled gait phases from a time series of gait data.

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Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

Sensors ◽

10.3390/s21041347 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1347

Author(s):

Long Liu ◽

Huihui Wang ◽

Haorui Li ◽

Jiayi Liu ◽

Sen Qiu ◽

...

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Gait Analysis ◽

Hidden Markov ◽

Inertial Sensor ◽

Outdoor Environment ◽

Human Gait ◽

Velocity Signal ◽

Inspection Method ◽

Gait Phase

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

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