Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

Marfusion: An Attention-Based Multimodal Fusion Model for Human Activity Recognition in Real-World Scenarios

Human Activity Recognition(HAR) plays an important role in the field of ubiquitous computing, which can benefit various human-centric applications such as smart homes, health monitoring, and aging systems. Human Activity Recognition mainly leverages smartphones and wearable devices to collect sensory signals labeled with activity annotations and train machine learning models to recognize individuals’ activity automatically. In order to deploy the Human Activity Recognition model in real-world scenarios, however, there are two major barriers. Firstly, sensor data and activity labels are traditionally collected using special experimental equipment in a controlled environment, which means fitting models trained with these datasets may result in poor generalization to real-life scenarios. Secondly, existing studies focus on single or a few modalities of sensor readings, which neglect useful information and its relations existing in multimodal sensor data. To tackle these issues, we propose a novel activity recognition model for multimodal sensory data fusion: Marfusion, and an experimental data collection platform for HAR tasks in real-world scenarios: MarSense. Specifically, Marfusion extensively uses a convolution structure to extract sensory features for each modality of the smartphone sensor and then fuse the multimodal features using the attention mechanism. MarSense can automatically collect a large amount of smartphone sensor data via smartphones among multiple users in their natural-used conditions and environment. To evaluate our proposed platform and model, we conduct a data collection experiment in real-life among university students and then compare our Marfusion model with several other state-of-the-art models on the collected datasets. Experimental Results do not only indicate that the proposed platform collected Human Activity Recognition data in the real-world scenario successfully, but also verify the advantages of the Marfusion model compared to existing models in Human Activity Recognition.

Effects of group housing and incremental hay supplementation in calf starters at different ages on growth performance, behavior, and health

The present study examined the effects of age at group housing and age at incremental hay supplementation in calf starters from 7.5 to 15% (dry matter, DM) and their interaction on growth performance, behavior, health of dairy calves, and development of heifers through first breeding. A total of 64 calves (n = 16 calves/treatment, 8 male and 8 female) were randomly assigned to 4 treatments in a 2 × 2 factorial arrangement, with age at group housing (early = d 28 ± 2, EG vs. late = d 70 ± 2, LG; 4 calves per group) and age at incremental hay supplementation of calf starters from 7.5 to 15% of DM (early = d 42 ± 2 d, EH vs. late = d 77 ± 2, LH) as the main factors. All calves (female and male) were weaned at 63 days of age and observed until 90 days of age. Heifer calves were managed uniformly from 90 days of age until first calving to evaluate the long-term effects of treatment. No interactions were observed between age at group housing and age at incremental hay to calves on starter feed intake, performance, calf health and behavior, and heifer development through first breeding, which was contrary to our hypothesis. The age at which incremental hay supplementation was administered had no effect on starter feed intake, growth performance, or heifer development until first calving. When EG calves were compared with LG calves, nutrient intake (starter, total dry matter, metabolizable energy, neutral detergent fiber, starch, and crude protein), average daily gain, and final body weight increased. In addition, frequency of standing decreased and time and frequency of eating increased in EG calves compared to LG calves. Overall, early group housing leads to improved growth performance in dairy calves with no negative effects on calf health compared to late group housing.

Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders

Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition.