This paper presents a human gait data collection for analysis and activity recognition consisting of continues recordings of combined activities, such as walking, running, taking stairs up and down, sitting down, and so on; and the data recorded are segmented and annotated. Data were collected from a body sensor network consisting of six wearable inertial sensors (accelerometer and gyroscope) located on the right and left thighs, shins, and feet. Additionally, two electromyography sensors were used on the quadriceps (front thigh) to measure muscle activity. This database can be used not only for activity recognition but also for studying how activities are performed and how the parts of the legs move relative to each other. Therefore, the data can be used (a) to perform health-care-related studies, such as in walking rehabilitation or Parkinson's disease recognition, (b) in virtual reality and gaming for simulating humanoid motion, or (c) for humanoid robotics to model humanoid walking. This dataset is the first of its kind which provides data about human gait in great detail. The database is available free of charge https://github.com/romanchereshnev/HuGaDB.
Recent human activity recognition (HAR) methods, based on on-body inertial sensors, have achieved increasing performance; however, this is at the expense of longer CPU calculations and greater energy consumption. Therefore, these complex models might not be suitable for real-time prediction in mobile systems, e.g., in elder-care support and long-term health-monitoring systems. Here, we present a new method called RapidHARe for real-time human activity recognition based on modeling the distribution of a raw data in a halfsecond context window using dynamic Bayesian networks. Our method does not employ any dynamic-programming-based algorithms, which are notoriously slow for inference, nor does it employ feature extraction or selection methods. In our comparative tests, we show that RapidHARe is an extremely fast predictor, one and a half times faster than artificial neural networks (ANNs) methods, and more than eight times faster than recurrent neural networks (RNNs) and hidden Markov models (HMMs). Moreover, in performance, RapidHare achieves an F1 score of 94.27% and accuracy of 98.94%, and when compared to ANN, RNN, HMM, it reduces the F1-score error rate by 45%, 65%, and 63% and the accuracy error rate by 41%, 55%, and 62%, respectively. Therefore, RapidHARe is suitable for real-time recognition in mobile devices. * Correspondence to akerteszfarkas@hse.ru arXiv:1809.09412v1 [cs.CY] 25 Sep 2018 on activities related to or performed by legs, such as walking, jogging, turning left or right, jumping, lying down, going up or down the stairs, sitting down, and so on. Human gait analysis (HGA) focuses not only on the recognition of activities observed but also on how activities are performed. This can be useful in health-care systems for monitoring patients recovering after surgery, fall detection, or diagnosing the state of, for example, Parkinson's disease [5,9,10]. An important application in HGA is installing body accelerometers on the hips and legs of people with Parkinson's disease [11]. Here, the objective is to detect freezing of the gait and prevent falling incidents.Our research group generally focuses on developing methods related to HGA and HAR, and in this article we were interested in and studied HAR methods, which have the following properties:1. Low prediction latency.2. Smooth, continuous activity recognition within a given activity and rapid transition in between different activities.3. Speed and energy efficiency for mobile-pervasive technologies.The first requirement ensures that the model is of low latency; therefore, activity prediction can be made instantly based on the latest observed data. Therefore, bidirectional models, such as bidirectional long short-term memory (LSTM) recurrent neural networks (RNN) [12] or dynamic time warping (DTW) [13] methods, are not appropriate for our aims for two main reasons: First, these bidirectional methods require a whole observed sequence before making any predictions, which would therefore increase their latency. Second, the prediction they make on a frame ...
Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the legs, which can be applied in prosthesis to imitate the missing part of the leg in walking. In particular, we propose a method, called GaIn, to control non-invasive, robotic, prosthetic legs. GaIn can infer the movements of both missing shanks and feet for humans suffering from double trans-femoral amputation using biologically inspired recurrent neural networks. Predictions are performed for casual walking related activities such as walking, taking stairs, and running based on thigh movement. In our experimental tests, GaIn achieved a 4.55° prediction error for shank movements on average. However, a patient’s intention to stand up and sit down cannot be inferred from thigh movements. In fact, intention causes thigh movements while the shanks and feet remain roughly still. The GaIn system can be triggered by thigh muscle activities measured with electromyography (EMG) sensors to make robotic prosthetic legs perform standing up and sitting down actions. The GaIn system has low prediction latency and is fast and computationally inexpensive to be deployed on mobile platforms and portable devices.
Training of deep models for classification tasks is hindered by local minima problems and vanishing gradients, while unsupervised layerwise pretraining does not exploit information from class labels. Here, we propose a new regularization technique, called diversifying regularization (DR), which applies a penalty on hidden units at any layer if they obtain similar features for different types of data. For generative models, DR is defined as divergence over the variational posteriori distributions and included in the maximum likelihood estimation as a prior. Thus, DR includes class label information for greedy pretraining of deep belief networks which result in a better weight initialization for fine-tuning methods. On the other hand, for discriminative training of deep neural networks, DR is defined as a distance over the features and included in the learning objective. With our experimental tests, we show that DR can help the backpropagation to cope with vanishing gradient problems and to provide faster convergence and smaller generalization errors.
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