Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment’s length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm’s worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time.

In sensor-based activity recognition often huge amounts of data have to be acquired from multiple sensors, which need to be communicated for further processing. When using wireless sensor nodes, energy efficiency is of outstanding importance, since it directly influences the time until the battery needs to be recharged. However, communicating a huge amount of sensor data over wireless interfaces causes high energy consumption as well. Furthermore, the host controller receiving the sensor data is hindered of using its low power modes, as it needs to be woken up more frequently as well. In the paper at hand, we introduce our approach of reducing energy consumption of sensor nodes in online activity recognition scenarios by calculating the feature extraction on the sensor subsystem itself. By doing so, we can reduce the output rate of the sensor which enables the host controller to stay in its low power modes for longer periods. Additionally, this approach drastically reduces the amount of data to be transmitted over wireless interfaces, which further improves energy consumption. In our experiments, the proposed approach reduces the energy consumption of a sensor node by up to 33 %.

The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

In the past decade, inertial measurement sensors have found their way into many wearable devices where they are used in a broad range of applications, including fitness tracking, step counting, navigation, activity recognition, or motion capturing. One of their key features that is widely used in motion capturing applications is their capability of estimating the orientation of the device and, thus, the orientation of the limb it is attached to. However, tracking a human’s motion at reasonable sampling rates comes with the drawback that a substantial amount of data needs to be transmitted between devices or to an end point where all device data is fused into the overall body pose. The communication typically happens wirelessly, which severely drains battery capacity and limits the use time. In this paper, we introduce fastSW, a novel piecewise linear approximation technique that efficiently reduces the amount of data required to be transmitted between devices. It takes advantage of the fact that, during motion, not all limbs are being moved at the same time or at the same speed, and only those devices need to transmit data that actually are being moved or that exceed a certain approximation error threshold. Our technique is efficient in computation time and memory utilization on embedded platforms, with a maximum of 210 instructions on an ARM Cortex-M4 microcontroller. Furthermore, in contrast to similar techniques, our algorithm does not affect the device orientation estimates to deviate from a unit quaternion. In our experiments on a publicly available dataset, our technique is able to compress the data to $10\%$ of its original size, while achieving an average angular deviation of approximately 2° and a maximum angular deviation below 9°.

Mobile devices have become ubiquitous, powerful computing devices. While their use scenarios require new input methods, their typical many-core computing architectures allow for new ways to implement these input methods. In this paper the suitability of many-core digital signal processors for online hand gesture recognition is evaluated. To this end, a system consisting of a data glove with three accelerometers and a many-core digital signal processor board is presented. Experiments assess realtime properties in hand gesture recognition on the many-core processing platform.