Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning- based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root- mean-squared-error (RMSE) of estimated walking speed is 0.16m&#x002F;s which is comparable to the results obtained by state-of- the-art approaches based on a number of body- worn sensors (i.e., RMSE of 0.11m&#x002F;s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.
The deep learning technology has been increasingly used in many recent works @cite_17 @cite_11 @cite_6 . However, these deep learning based systems are focused on recognition of gait patterns rather than estimation of walking speed. Gong proposed a DCNN to perform gait assessment for multiple sclerosis patients based on the spectral and temporal associations among sensor data collected with a number of inertial body sensors @cite_17 . Gadaleta and Rossi adopted the DCNN to recognize a target user based on the way of their walking utilizing the accelerometer and gyroscope data of smartphone @cite_11 . Hannink used the DCNN to estimate the stride length @cite_6 . In line of this research direction that is built upon deep learning technology, we propose the first general-purpose deep learning based walking speed estimation framework for smartphone.
@cite_11 @cite_6 @cite_17
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Walking impairment resulted by various chronic diseases, disorders and injuries have been investigated using recent emerging wearable technology, for instance, gait assessment using inertial body sensors in 6-minute walk (6MW) for persons with Multiple Sclerosis (PwMS) to identify spatiotemporal features useful to assess MS progression. However, most studies to date have investigated the features extracted from movements of the lower limbs and do not provide a holistic gait assessment. A recent pilot study demonstrated that the holistic gait assessment such as evaluating the associations among lower and upper limbs provided better discrimination between healthy controls and PwMS. This paper is motivated by this and further aim to answer the following question: can we identify the temporal gait patterns in terms of the holistic gait assessment? Traditionally this suffers from the statistical property of the causality inference method adopted by previous study. We proposed a deep convolutional neural network (CNN) to learn the temporal and spectral associations among the time-series motion data captured by the inertial body sensors. A simulated model was developed to train the CNN, and then the trained CNN was adopted to assess the gait performance from a pilot dataset with 41 subjects (28 PwMS and 13 healthy controls). Experimental results are reported to illustrate the performance of the proposed approach. Recently, Deep Convolutional Neural Networks (DCNNs) have been applied to the task of human pose estimation, and have shown its potential of learning better feature representations and capturing contextual relationships. However, it is difficult to incorporate domain prior knowledge such as geometric relationships among body parts into DCNNs. In addition, training DCNN-based body part detectors without consideration of global body joint consistency introduces ambiguities, which increases the complexity of training. In this paper, we propose a novel end-to-end framework for human pose estimation that combines DCNNs with the expressive deformable mixture of parts. We explicitly incorporate domain prior knowledge into the framework, which greatly regularizes the learning process and enables the flexibility of our framework for loopy models or tree-structured models. The effectiveness of jointly learning a DCNN with a deformable mixture of parts model is evaluated through intensive experiments on several widely used benchmarks. The proposed approach significantly improves the performance compared with state-of-the-art approaches, especially on benchmarks with challenging articulations. This paper analyses gait patterns of patients with Parkinson;s Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems. Deep Convolutional Neural Networks (DCNN) have been proven to be effective for various computer vision problems. In this work, we demonstrate its effectiveness on a continuous object orientation estimation task, which requires prediction of 0 to 360 degrees orientation of the objects. We do so by proposing and comparing three continuous orientation prediction approaches designed for the DCNNs. The first two approaches work by representing an orientation as a point on a unit circle and minimizing either L2 loss or angular difference loss. The third method works by first converting the continuous orientation estimation task into a set of discrete orientation estimation tasks and then converting the discrete orientation outputs back to the continuous orientation using a mean-shift algorithm. By evaluating on a vehicle orientation estimation task and a pedestrian orientation estimation task, we demonstrate that the discretization-based approach not only works better than the other two approaches but also achieves state-of-the-art performance. We also demonstrate that finding an appropriate feature representation is critical to achieve a good performance when adapting a DCNN trained for an image recognition task.