Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition

Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition due to its ability of modeling the temporal information in various ranges of dynamic contexts. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions change over time. To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people. Specifically, for each frame, two sub-memory units store individual motion information, while a concurrent LSTM unit selectively integrates and stores inter-related motion information between interacting people from these two sub-memory units via a new co-memory cell. Experimental results on the BIT and UT datasets show the superiority of Co-LSTSM compared with the state-of-the-art methods

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions

Exploring the diagnostic accuracy of the KidFit screening tool for identifying children with health and motor performance-related fitness impairments: A feasibility study

Child obesity is associated with poor health and reduced motor skills. This study aimed to assess the diagnostic accuracy of the KidFit Screening Tool for identifying children with overweight/obesity, reduced motor skills and reduced cardiorespiratory fitness. Fifty-seven children (mean age: 12.57 ± 1.82 years; male/female: 34/23) were analysed. The Speed and Agility Motor Screen (SAMS) and the Modified Shuttle Test-Paeds (MSTP) made up the KidFit Screening Tool. Motor Proficiency (BOT2) (Total and Gross) was also measured. BMI, peak-oxygen-uptake (VO2peak) were measured with a representative sub-sample (n = 25). Strong relationships existed between the independent variables included in the KidFit Screening Tool and; BMI (R2 = 0.779, p < 0.001); Gross Motor Proficiency (R2 = 0.612, p < 0.001) and VO2peak (mL/kg/min) (R2 = 0.754, p < 0.001). The KidFit Screening Tool has a correct classification rate of 0.84 for overweight/obesity, 0.77 for motor proficiency and 0.88 for cardiorespiratory fitness. The sensitivity and specificity of the KidFit Screening Tool for identifying children with overweight/obesity was 100% (SE = 0.00) and 78.95%, respectively (SE = 0.09), motor skills in the lowest quartile was 90% (SE = 0.095) and 74.47% (SE = 0.064), respectively, and poor cardiorespiratory fitness was 100% (SE = 0.00) and 82.35% (SE = 0.093), respectively. The KidFit Screening Tool has a strong relationship with health- and performance-related fitness, is accurate for identifying children with health- and performance-related fitness impairments and may assist in informing referral decisions for detailed clinical investigations