RapidHARe: A computationally inexpensive method for real-time human activity recognition from wearable sensors

Abstract

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 ...

GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering From Double Trans-Femoral Amputation

“…For activity recognition, we used the Rapid-HARe model [25], which is a computationally inexpensive method for providing a smooth and accurate activity prediction with low prediction latency. It is based on a dynamic Bayesian network [28], illustrated in Figure 4, and the most likely activity s t being performed at time t with respect to a given data observed in a context window…”

“…The training of GMMs was straightforward because our training data were segmented. For the full derivation of the model, we refer the reader to our previous work [25].…”

“…In other words, bidirectional methods use data from the future to make a prediction in the present. This would increase the prediction latency [25].…”

“…In addition, we designed GaIn to be fast and computationally inexpensive, performing low prediction latency. In our opinion, these features are necessary in order to be applied on mobile devices where energy consumption matters [25]. We note that turning during walking involves rotating the torso, hip, and the thighs at hip joints but not the shanks [26]; therefore, our analysis does not examine turning strategies.…”

GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering from Double Trans-Femoral Amputation

“…In addition, we designed GaIn to be fast and computationally inexpensive, performing low prediction latency. In our opinion, these features are necessary in order to be applied on mobile devices where energy consumption matters [26]. We note that turning during walking involves rotating the torso, hip, and the thighs at hip joints but not the shanks [27]; therefore, our analysis does not examine turning strategies.…”