The next era of technology is approaching very rapidly. Wearable devices such as smart glasses and watches are ushering in this new age. These devices are capable of overlaying tons of information on the user’s current view of the world, such as overlaying maps, street names, and directions to a destination while the user is driving or walking. Another example is the processing capabilities of smart watches that enable them to run various applications, such as computing the distance the user walked or ran. In addition to the general-purpose computing unit found in most of these wearable devices, you may also find MEMS inertial sensor triads such as accelerometers, gyroscopes, and magnetometer triads. The advantages of using MEMS technology are that it is small and lightweight, and the technology’s low power consumption facilitates including it in most of today’s portable devices, where battery life is of big concern. Finally, and most importantly, the technology being low cost has helped it spread into most smart phones and tablets. These sensors are mainly included for entertainment and gaming or enabling a free-hand interaction between the user and the device. For example, when the user performs a certain gesture, one or more of these sensors is used to capture this gesture and a corresponding command is executed in response. These sensors can be used also for navigation purposes, but due to the low grade of these low-cost commercial sensors, they aren’t very accurate and the error drifts rapidly. Further enhancements by innovative algorithms are needed rather than using these sensors readings directly for navigation purposes. Portable devices such as smart phones and tablets are witnessing a great technological revolution as well, with portable devices sales overtaking those of traditional desktop computers. Using these portable devices one can read the news and local forecast, get directions to a nearby restaurant, or reroute their commute to work around heavy traffic. All these services can be provided due to the advancements taking place and the incorporation of Global Navigation Satellite Systems (GNSS) receivers and inertial sensors triads in most modern portable devices. This revolution in portable devices has aided in the emergence of the “application accessory” or “appcessory” for short. Appcessory is a new term used to describe an accessory for a portable device that is specific for a certain application, which can use Wi-Fi or Bluetooth for connection with its portable device. The appcessory represents another shift in portable devices technology and many appcessories are available for most of today’s portable devices. Appcessory’s such as belt clips and digital wristwatches are connected to the portable device to monitor user physical such as heart rate or calculating how many calories the user burned during an activity. Such appcessories also include one or more inertial sensors triads such as those found in wearable computing devices or portable devices as mentioned above. The main challenge remains that the low-cost MEMS sensors in current wearable computing devices, appcessories, or even portable devices such as latest smartphones are considered insufficient for reliable navigation purposes. This is due to very high noise and large random drift rates, especially for mobile devices that can freely change orientation with respect to the user. The prior solution to overcoming sensor error is better error modeling, however this is harder for portable navigation where devices have the additional problem of varying orientation with respect to the user. Given the fact that it is likely that a user will have multiple devices among the abovementioned groups (i.e. wearable computing devices, portable devices such as smartphones/tablets, and appcessories), the use of the sensors triads on the different available devices can be exploited to enhance the navigation solution through the use of multi-triads (e.g. multi-IMU’s). At least one device, whether a wearable computing device such as glasses or portable device such as smart phone/tablet, which possesses enough computing power to run the proposed technique has to be present. The sensors in this device can be integrated with the sensors in one or more other devices such as wearable computing devices like a smart watch, portable devices such as a tablet, or appcessories like a belt clip or keychain to produce a higher accuracy navigation solution. This paper proposes a multiple sensors’ triads solution and the underlying techniques to benefit from this redundancy of sensors to get better navigation results. The proposed solution exploits the fact that multiple of the aforementioned devices will be carried by the same user and wirelessly connected together. Benefiting from the multiple triads and integrating them boosts the final results substantially.

The techniques presented in this paper show how integrating multiple sensors triads transform the available low-cost sensors into navigation capable sensors. In summary, the major factor that distinguishes the proposed solution from other traditional inertial navigation solutions is that it exploits the connection already present between most of today’s electronic devices already carried by the same user. Whether a wearable computing device, portable device or appcessories, the solution integrates all the information coming from this connected network of multiple sensors triads and provides a final enhanced navigation solution. This final solution is reliable and accurate, whether on foot or driving, in different environments such indoors or outdoors in open sky or an urban canyon. Many datasets were collected for testing and verification. These datasets were collected by different users who varied in age, gender, and other characteristics. The datasets were collected using two or more sensor triads, in devices carried at different positions on users’ body such as on their waist, wrist, head, chest, and ear, as well as in the users hand or pocket. The results were analyzed, and they clearly demonstrated that a much more reliable and accurate navigation solution can be obtained by integrating multiple sensors triads found in many devices already carried by consumers.

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