Sensor-based activity recognition with dynamically added context

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods

Towards Knowledge Infusion for Robust and Transferable Machine Learning in IoT

Machine learning (ML) applications in Internet of Things (IoT) scenarios face the issue that supervision signals, such as labeled data, are scarce and expensive to obtain. For example, it often requires a human to manually label events in a data stream by observing the same events in the real world. In addition, the performance of trained models usually depends on a specific context: (1) location, (2) time and (3) data quality. This context is not static in reality, making it hard to achieve robust and transferable machine learning for IoT systems in practice. In this paper, we address these challenges with an envisioned method that we name Knowledge Infusion. First, we present two past case studies in which we combined external knowledge with traditional data-driven machine learning in IoT scenarios to ease the supervision effort: (1) a weak-supervision approach for the IoT domain to auto-generate labels based on external knowledge (e.g., domain knowledge) encoded in simple labeling functions. Our evaluation for transport mode classification achieves a micro-F1 score of 80.2%, with only seven labeling functions, on par with a fully supervised model that relies on hand-labeled data. (2) We introduce guiding functions to Reinforcement Learning (RL) to guide the agents' decisions and experience. In initial experiments, our guided reinforcement learning achieves more than three times higher reward in the beginning of its training than an agent with no external knowledge. We use the lessons learned from these experiences to develop our vision of knowledge infusion. In knowledge infusion, we aim to automate the inclusion of knowledge from existing knowledge bases and domain experts to combine it with traditional data-driven machine learning techniques during setup/training phase, but also during the execution phase

気圧高度を用いた足部の高さ測定のための靴搭載型デバイスの開発

本研究では，日常的な運動可能性の拡大，運動負荷の確保を目的に，靴に搭載可能，かつ歩行時の足の高さを計測可能な活動量計の開発を目指していた．その過程で，現時点では静止状態や擬似階段において高さ方向に数～数十cmのオーダーで計測が可能であること，平地歩行時や階段歩行時では高さの計測はできなかったが，足の動作による高さ方向の変化をとることができた．気圧と高度の関係について述べた後，実際に1つの気圧センサを使い気圧高度を求めることをおこなった．しかしながら，結果としては気圧高度の値が設置した高さの真値から最大35cm異なっていた．改善策として2つの気圧センサを使うことを検討し，その際発生する2つの気圧センサの個体差を埋めるための対策について以下の3つのことを検討した．1.補正の方法として2つの気圧センサを同じ高さに設置し数秒間の計測の後，平均をとりその差を用いることを提案した．2.センサそれぞれの持つ温度センサの取得する値の違いにより発生していた個体差について温度センサの値を共有することで解決を図った．3.ケースに格納することによって，気圧センサがそれぞれ違う空間となってしまいケース内での気圧を計測してしまうことによって個体差が発生していた．これは2つの気圧センサを同一な空間に設置することで解決した．静止状態における気圧高度の計測がある程度おこなえるようになったため，歩行時における気圧高度の計測を行った．歩行に関しては平地歩行，擬似階段歩行，階段歩行の全部で3つの状態で実験をおこなった．擬似階段歩行については高さの真値との誤差が数cmに収まっていた．しかし，平地歩行や階段歩行における気圧高度による足部の高さを測定することができなかった．これはセンサの向きや足の振り方などいくつか原因が考えられるため，今後はそれぞれの要素に対して検証をおこなっていく必要があると考えられる． 階段部での歩行における足部の高さ計測をもとに踏み台運動支援のためのアプリケーションを作成した．ユーザ評価を行った結果，達成感やモチベーションの改善が多少見られた．電気通信大学201

Positioning Commuters And Shoppers Through Sensing And Correlation

Positioning is a basic and important need in many scenarios of human daily activities. With position information, multifarious services could be vitalized to benefit all kinds of users, from individuals to organizations. Through positioning, people are able to obtain not only geo-location but also time related information. By aggregating position information from individuals, organizations could derive statistical knowledge about group behaviors, such as traffic, business, event, etc. Although enormous effort has been invested in positioning related academic and industrial work, there are still many holes to be filled. This dissertation proposes solutions to address the need of positioning in people’s daily life from two aspects: transportation and shopping. All the solutions are smart-device-based (e.g. smartphone, smartwatch), which could potentially benefit most users considering the prevalence of smart devices. In positioning relevant activities, the components and their movement information could be sensed by different entities from diverse perspectives. The mechanisms presented in this dissertation treat the information collected from one perspective as reference and match it against the data collected from other perspectives to acquire absolute or relative position, in spatial as well as temporal dimension. For transportation, both driver and passenger oriented solutions are proposed. To help drivers improve safety and ease the tension from driving, two correlated systems, OmniView [1] and DriverTalk [2], are provided. These systems infer the relative positions of the vehicles moving together by matching the appearance images of the vehicles seen by each other, which help drivers maintain safe distance from surrounding vehicles and also give them opportunities to precisely convey driving related messages to targeted peer drivers. To improve bus-riding experience for passengers of public transit systems, a system named RideSense [3] is developed. This system correlates the sensor traces collected by both passengers’ smart devices and reference devices in buses to position passengers’ bus-riding, spatially and temporally. With this system, passengers could be billed without any explicit interaction with conventional ticketing facilities in bus system, which makes the transportation system more efficient. For shopping activities, AutoLabel [4, 5] comes into play, which could position customers with regard to stores. AutoLabel constructs a mapping between WiFi vectors and semantic names of stores through correlating the text decorated inside stores with those on stores’ websites. Later, through WiFi scanning and a lookup in the mapping, customers’ smart devices could automatically recognize the semantic names of the stores they are in or nearby. Therefore, AutoLabel-enabled smart device serves as a bridge for the information flow between business owners and customers, which could benefit both sides

Investigation of Context Determination for Advanced Navigation using Smartphone Sensors

Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning, while the behaviour can contribute additional information to the navigation solution. Although many navigation and positioning techniques have been developed, no single one is capable of providing reliable and accurate positioning in all contexts. Therefore, it is necessary for a navigation system to be able to operate across different types of contexts. Context adaptive navigation offers a solution to this problem by detecting the operating contexts and adopting different positioning techniques accordingly. This study focuses on context determination with the available sensors on smartphone, through framework design, behavioural and environmental context detection, context association, comprehensive experimental tests, and system demonstration, building the foundation for a context-adaptive navigation system. In this thesis, the overall framework of context determination is first designed. Following the framework, the behavioural contexts, covering different human activities and vehicle motions, are recognised by different machine learning classifiers in hierarchy. Their classification results are further enhanced by feature selection and a connectivity dependent filter. Environmental contexts are detected from GNSS measurements. Indoor and outdoor environments are first distinguished based on the availability and strength of GNSS signals using a hidden Markov model based method. Within the model, the different levels of connections between environments are exploited as well. Then a fuzzy inference system is designed to enable the further classification of outdoor environments into urban and open-sky. As behaviours and environments are not completely independent, this study also considers context association, investigating how behaviours can be associated within environment detection. Tests in a series of multi-context scenarios have shown that the association mechanism can further improve the reliability of context detection. Finally, the proposed context determination system has been demonstrated in daily scenarios