Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking

Hu, Baotian; Dixon, Philippe C.; Jacobs, Jan; Dennerlein, Jack T.; Schiffman, Jeffrey M.

doi:10.1016/j.jbiomech.2018.01.005

Cited by 79 publications

(52 citation statements)

References 30 publications

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“…The goal of these approaches was to estimate the movements in the video frame sequences. For using the sensory data, Hu et al [23] proposed a method to investigate the performance of the deep learning network with long short-term memory (LSTM) units to deal with the sensory value of an inertial motion unit (IMU). They verified that machine-learning approaches are able to detect the surface conditions of the road and age-group of the subjects from the sensory data collected from the walking behavior of the subjects.…”

Section: Deep Learning-based Movement Estimation Approachesmentioning

confidence: 99%

“…Table 1, it can be seen that there are mainly two types of conventional and widespread motion capture methods. These methods can be classified into image-based methods [18][19][20], which estimate the movement based on convolutional neural networks (CNNs) [23], and sensor-based methods, which use Bayesian probability [11,14,15] and LSTM [23]. In [11,14,15], the movements were estimated using Bayesian probability, whereas in [23], the surface conditions of the road and age-group of the subjects were detected based on the sensor values and an LSTM network.…”

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

“…These methods can be classified into image-based methods [18][19][20], which estimate the movement based on convolutional neural networks (CNNs) [23], and sensor-based methods, which use Bayesian probability [11,14,15] and LSTM [23]. In [11,14,15], the movements were estimated using Bayesian probability, whereas in [23], the surface conditions of the road and age-group of the subjects were detected based on the sensor values and an LSTM network. Owing to the significant contribution of deep learning methods in the field of motion capture, this study is expected to bring forth a deep learning-based framework, instead of the traditional methods [11,14,15], to improve the performance of VR applications using soft sensors.…”

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

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Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Guo

Sung

2020

Sensors

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The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

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Section: Deep Learning-based Movement Estimation Approachesmentioning

confidence: 99%

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

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Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Guo

Sung

2020

Sensors

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“…The results of the stability prediction are evaluated by three indexes: precision, recall, and f1score (known as the harmonic mean of precision and recall [38]), which are as follows:…”

Section: Judgment Of Transient Voltage Stabilitymentioning

confidence: 99%

Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

et al. 2019

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The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

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“…System. Different deep learning methods have been successfully used for fall detection [2,[25][26][27][28]. Analyzing those systems, we see that all these methods rely either on models with a huge number of parameters or on remote communication.…”

Section: Fall Detection With Deep Learning For Embeddedmentioning

confidence: 99%

Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations

Torti

Musci

Guareschi

et al. 2019

Scientific Programming

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Accidental falls are the main cause of fatal and nonfatal injuries, which typically lead to hospital admissions among elderly people. A wearable system capable of detecting unintentional falls and sending remote notifications will clearly improve the quality of the life of such subjects and also helps to reduce public health costs. In this paper, we describe an edge computing wearable system based on deep learning techniques. In particular, we give special attention to the description of the classification and communication modules, which have been developed by keeping in mind the limits in terms of computational power, memory occupancy, and power consumption of the designed wearable device. e system thus developed is capable of classifying 3Daccelerometer signals in real-time and to issue remote alerts while keeping power consumption low and improving on the present state-of-the-art solutions in the literature.

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Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking

Cited by 79 publications

References 30 publications

Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations

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