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.
A number of smartphone-based systems have been developed @cite_16 @cite_8 @cite_0 . Cox proposed a simple solution that estimates the walking speed based on the integration of acceleration @cite_16 . Cho proposed to calibrate opportunistically the inertial sensor-based speed estimation using GPS of smartphone when the user is walking outdoors @cite_0 . Park applied the regularized kernel methods on the collected accelerometer data to achieve higher accuracy of walking speed estimation @cite_8 . Although there have been efforts to utilize machine learning techniques to improve the walking speed estimation accuracy, identification of effective features and manual extraction of those features are very challenging. In contrast to other smartphone based approaches, we leverage automated extraction of the most effective features using the DCNN to maximize the walking speed estimation accuracy.
@cite_0 @cite_16 @cite_8
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AutoGait is a mobile platform that autonomously discovers a user's walking profile and accurately estimates the distance walked. The discovery is made by utilizing the GPS in the user's mobile device when the user is walking outdoors. This profile can then be used both indoors and outdoors to estimate the distance walked. To model the person's walking profile, we take advantage of the fact that a linear relationship exists between step frequency and stride length, which is unique to individuals and applies to everyone regardless of age. Autonomous calibration invisible to users allows the system to maintain a high level of accuracy under changing conditions. AutoGait can be integrated into any pedometer or indoor navigation software on handheld devices as long as they are equipped with GPS. The main contribution of this paper is two fold: (1) we propose an auto-calibration method that trains a person's walking profile by effectively processing noisy GPS readings, and (2) we build a prototype system and validate its performance by performing extensive experiments. Our experimental results confirm that the proposed auto-calibration method can accurately estimate a person's walking profile and thus significantly reduce the error rate. Each year, 15 million people suffer stroke worldwide. Among them, 5 million die and another 5 million are permanently disabled. Stroke recovery is a lifelong process. Clinical research have shown that gait velocity (a.k.a, walking speed) is a very powerful indicator of function and prognosis after stroke. In this paper, we focus on developing new algorithms to estimate the walking speed using pervasive devices, such as smartphone. While there are existing techniques to measure walking speed using inertial sensors, very little research has specifically involved smartphone, due to some unique challenges caused by pervasive devices, such as placement of the sensor and sensor drifting. We propose new practical algorithms based on high pass filter, integration of accelerator's reminder, and feedback loop. We evaluate our proposed approach with real world data and present a through analysis on the results. The experimental results have indicated that proposed approach is a promising practical approach for gait speed estimation. We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94 accuracy compared to 82 for previous work, and our walking speed estimates are within 12-15 (straight indoor walk) of ground truth compared to 17-22 for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.