A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device’s functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented.

Recently, animal welfare has gained worldwide attention. The concept of animal welfare encompasses the physical and mental well-being of animals. Rearing layers in battery cages (conventional cages) may violate their instinctive behaviors and health, resulting in increased animal welfare concerns. Therefore, welfare-oriented rearing systems have been explored to improve their welfare while maintaining productivity. In this study, we explore a behavior recognition system using a wearable inertial sensor to improve the rearing system based on continuous monitoring and quantifying behaviors. Supervised machine learning recognizes a variety of 12 hen behaviors where various parameters in the processing pipeline are considered, including the classifier, sampling frequency, window length, data imbalance handling, and sensor modality. A reference configuration utilizes a multi-layer perceptron as a classifier; feature vectors are calculated from the accelerometer and angular velocity sensor in a 1.28 s window sampled at 100 Hz; the training data are unbalanced. In addition, the accompanying results would allow for a more intensive design of similar systems, estimation of the impact of specific constraints on parameters, and recognition of specific behaviors.

The people-centric sensing community is paying substantial attention to the smartphone as an ad hoc, low-cost, and dense sensing method because it permits people to participate easily in sensing activities, i.e., just by carrying it as usual. People carry their smartphones in various manners, rendering measurement results unreliable. For example, humidity is typically higher in a trouser pocket than around the neck as a result of sweat. In this article, we propose a platform for people-centric sensing that considers the on-body position of a smartphone as metadata. A general architecture is presented, and a universal serial bus-based external sensing module for an Android-based terminal is developed. A heatstroke alert map that visualizes the heatstroke risk is presented as an application based on both the collected raw data and metadata using the platform.

Augmented Reality (AR) is a class of “mediated reality” that artificially modifies the human perception by superimposing virtual objects on the real world, which is expected to supplement reality. In visual-based augmentation, text and graphics, i.e., label, are often associated with a physical object or a place to describe it. View management in AR is to maintain the visibility of the associated information and plays an important role on communicating the information. Various view management techniques have been investigated so far; however, most of them have been designed for two dimensional see-through displays, and few have been investigated for projector-based AR called spatial AR. In this article, we propose a view management method for spatial AR, VisLP, that places labels and linkage lines based on the estimation of the visibility. Since the information is directly projected on objects, the nature of optics such as reflection and refraction constrains the visibility in addition to the spatial relationship between the information, the objects, and the user. VisLP employs machine-learning techniques to estimate the visibility that reflects human’s subjective mental workload in reading information and objective measures of reading correctness in various projection conditions. Four classes are defined for a label, while the visibility of a linkage line has three classes. After 88 and 28 classification features for label and linkage line visibility estimators are designed, respectively, subsets of features with 15 and 14 features are chosen to improve the processing speed of feature calculation up to 170%, with slight degradation of classification performance. An online experiment with new users and objects showed that 76.0% of the system’s judgments were matched with the users’ evaluations, while 73% of the linkage line visibility estimations were matched.

Automatic detection of behaviors and locations has been increasingly needed in the management of noncage systems where hen behaviors are highly diverse and active. Here we show a technology to spatiotemporally understand behaviors using a wearable inertial sensor, containing a 3-axis accelerometer and 3-axis angular velocity sensor (i.e., gyroscope), and a marker. Using supervised machine learning, we first developed a tool that automatically classified and counted 11 behaviors, including comfort behaviors such as head scratching and tail-wagging with small movements as well as dust-bathing with dynamic movements. As expected, these behaviors were observed more frequently in floor pens than in conventional cages (all P < 0.01). We also spatially mapped the behaviors in floor pens and visualized the behavioral frequency in each resource by detecting the colored markers on the sensor. Furthermore, using the time-series information included in the sensor data, we analyzed the behavioral transition from one behavior to another. The behavioral transitions were more complex in floor pens, and the number was higher in body shaking, tail-wagging, resting, litter scratching, dust-bathing, preening, and moving in floor pens than in conventional cages (all P < 0.05). Our tools presented deeper insights into where and what hens behaved and also suggested that connectivity between behaviors, as well as observing the frequency of a behavior, can be an important indicator for welfare assessment in laying hens.