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In this work, we present a novel method to estimate joint angles and distance traveled by a human while walking. We model the human leg as a two-link revolute robot. Inertial measurement sensors placed on the thigh and shin provide the required measurement inputs. The model and inputs are then used to estimate the desired state parameters associated with forward motion using an extended Kalman filter (EKF). Experimental results with subjects walking in a straight line show that distance walked can be measured with accuracy comparable to a state of the art motion tracking systems. The EKF had an average RMSE of 7 cm over the trials with an average accuracy of greater than 97% for linear displacement.

In this paper, an orientation transformation (OT) algorithm is presented that increases the effectiveness of performing activity recognition using body sensor networks (BSNs). One of the main limitations of current recognition systems is the requirement of maintaining a known, or original, orientation of the sensor on the body. The proposed OT algorithm overcomes this limitation by transforming the sensor data into the original orientation framework such that orientation dependent recognition algorithms can still be used to perform activity recognition irrespective of sensor orientation on body. The approach is tested on an orientation dependent activity recognition system which is based on dynamic time warping (DTW). The DTW algorithm is used to detect the activities after the data is transformed by OT. The precision and recall for the activity recognition for five subjects and five movements was observed to range from 74% to 100% and from 83% to 100%, respectively. The correlation coefficient between the transformed data and the data from the original orientation is above 0.94 on axis with well-defined patterns.

The Internet of Things (IoT) is fueled by the growth of sensors, actuators, and services that collect and process raw sensor data. Wearable and environmental sensors will be a major component of the IoT and provide context about people and activities that are occurring. It is imperative that sensors in the IoT are synchronized, which increases the usefulness and value of the sensor data and allows data from multiple sources to be combined and compared. Due to the heterogeneous nature of sensors (e.g., synchronization protocols, communication channels, etc.), synchronization can be difficult. In this article, we present novel techniques for synchronizing data from multi-sensor environments based on the events and interactions measured by the sensors. We present methods to determine which interactions can likely be used for synchronization and methods to improve synchronization by removing erroneous synchronization points. We validate our technique through experiments with wearable and environmental sensors in a laboratory environment. Experiments resulted in median drift error reduction from 66% to 98% for sensors synchronized through physical interactions.

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