Different technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, fitting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant’s legs to capture accelerometer and gyroscope data, and 42 reflexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre-processed in each of two sessions, performed on different days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specific for each system.

Mobile devices are becoming ubiquitous and, sometimes, even extensions of ourselves. These devices are growing fast in terms of delivered computational power, storage capacity, battery duration, and built-in sensors. Time and again, we see headlines advertising new unforeseen applications leveraging this power, especially the sensors, for solving diverse problems, including fall detection, user's activity recognition, location identification, or even user authentication based on the way of walking (gait). In this paper, we focus on motion sensors and discuss how the provided data can be interpreted and transformed to better serve different purposes. We propose a method to process the data from such sensors that reduces the acquisition noise and possible artifacts, and turns the data invariant to the device's position and the user's movement direction. We introduce a new coordinate system referred to as user-centric, as opposed to the two most common coordinate systems used-the device and world-coordinate systems. For testing, we design and develop a user recognition system based on gait, in which the three coordinates systems are compared and discussed considering a one-versus-rest authentication setup. The results show the importance of properly pre-processing the acquired data to enable more reliable applications underpinned by mobile sensors.

Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live music concerts. It is essential though to clearly understand the capabilities and limitations of such acquisition systems and their potential influence on high-level control of musical processes. In this study, we evaluate one such system composed of an inertial sensor (MetaMotionR) and a hexaphonic nylon guitar for capturing strumming gestures. To characterize this system, we compared it with a high-end commercial motion capture system (Qualisys) typically used in the controlled environments of research laboratories, in two complementary tasks: comparisons of rotational and translational data. For the rotations, we were able to compare our results with those that are found in the literature, obtaining RMSE below 10° for 88% of the curves. The translations were compared in two ways: by double derivation of positional data from the mocap and by double integration of IMU acceleration data. For the task of estimating displacements from acceleration data, we developed a compensative-integration method to deal with the oscillatory character of the strumming, whose approximative results are very dependent on the type of gestures and segmentation; a value of 0.77 was obtained for the average of the normalized covariance coefficients of the displacement magnitudes. Although not in the ideal range, these results point to a clearly acceptable trade-off between the flexibility, portability and low cost of the proposed system when compared to the limited use and cost of the high-end motion capture standard in interactive music setups.

Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task, as it can aid law-enforcement institutions in fighting illegal drug dealers worldwide, while, at the same time, it can help monitor legalized crops in countries that regulate them. However, existing art on detecting drug crops from remote sensing images is limited in some key factors, not taking full advantage of the available hyperspectral information for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different convolutional neural network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in our experiments a dataset containing Cannabis Sativa crops, spotted by police operations in a Brazilian region called the Marijuana Polygon. The results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places in the same region are used for training and testing the methods, highlighting the promising use of this solution to aid police operations in detecting and collecting evidence of such sensitive content properly.

Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, empowering the automatic gait recognition field. Whereas gait recognition works usually focus on improving end-to-end performance measures, this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identification scenario. In this setup, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and differences between subjects in a verification setup. The explorations concluded that periodic components occurring in frequencies between 6 and 10 Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.