Kazuya Murao

A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption

In wearable computing environments, a wearable computer runs various applications using various sensors (wearable sensors). In the area of context awareness, though various systems using accelerometers have been proposed to recognize very minute motions and states, energy consumption was not taken into consideration. We propose a context-aware system that reduces energy consumption. In life, the granularity of required contexts differs according to the situation. Therefore, the proposed system changes the granularity of cognitive contexts of a users situation and supplies power on the basis of the optimal sensor combination. Higher accuracy is achieved with fewer sensors. In addition, in proportion to the remainder of power resources, the proposed system reduces the number of sensors within the tolerance of accuracy. Moreover, the accuracy is improved by considering context transition. Even if the number of sensors changes, no extra classifiers or training data are required because the data for shutting off sensors is complemented by our proposed algorithm. By using our system, power consumption can be reduced without large losses in accuracy.

A pedestrian flow analysis system using Wi-Fi packet sensors to a real environment

The authors have been developing the system, which analyzes pedestrian flow using Wi-Fi packet sensors. The sensors collect Wi-Fi packet called probe request packet, which is transmitted from a smartphone to search Wi-Fi access points. In addition, the cloud storage server is running to manage observed packets centrally and to compute pedestrian flow in real time. Additionally, user movement history is vitally important and we have to pay close attention to handling that kind of data. Therefore, the system runs with an anonymization method and a cryptographic function. Some kinds of demonstration experiments were held in real environment. As a result, it was confirmed that we can analyze the rough tendency of pedestrian flow using the present system and simple analysis methods.

HASC2011corpus: towards the common ground of human activity recognition

Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.

A motion recognition method by constancy-decision

Many context-aware systems using accelerometers have been proposed. Contexts that have been recognized are categorized into postures (e.g. sitting), behaviors (e.g. walking), and gestures (e.g. a punch). Postures and behaviors are states lasting for a certain length of time. Gestures, however, are sporadic or once-off actions. It has been a challenging task to find gestures buried in other contexts. In this paper, we propose a method that classifies contexts into postures, behaviors, and gestures by using the autocorrelation of the acceleration values and recognizes contexts with an appropriate method. We evaluated the recall and precision of recognition for seven kinds of gestures while five kinds of behaviors; The conventional method gave values of 0.75 and 0.59 whereas our method gave 0.93 and 0.93. Our system enables a user to input by gesturing even while he or she is performing a behavior.

Evaluating Gesture Recognition by Multiple-Sensor-Containing Mobile Devices

Mobile phones or video game controllers using gesture recognition technologies enable easy and intuitive operations. However, usually only one of each type of sensor is installed in each device, and the effect of multiple sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We captured data for 27 kinds of gestures by using a mobile device with 9 accelerometers and 9 gyroscopes, we then experimentally investigated the effects on recognition accuracy of changing the number and positions of sensors, and the number and kinds of gestures. The results showed that the use of multiple sensors and of sensors positioned at specific positions affects accuracy. It was also shown that gestures are interdependent and selecting specific gestures improves recognition accuracy.

Labeling method for acceleration data using an execution sequence of activities

In the area of activity recognition, many systems using accelerometers have been proposed. Common method for activity recognition requires raw data labeled with ground truth to learn the model. To obtain ground truth, a wearer records his/her activities during data logging through video camera or handwritten memo. However, referring a video takes long time and taking a memo interrupts natural activity. We propose a labeling method for activity recognition using an execution sequence of activities. The execution sequence includes activities in performed order, does not include time stamps, and is made based on his/her memory. Our proposed method partitions and classifies unlabeled data into segments and clusters, and assigns a cluster to each segment, then assign labels according to the best-matching assignment of clusters with the user-recorded activities. The proposed method gave a precision of 0.812 for data including seven kinds of activities. We also confirmed that recognition accuracy with training data labeled with our proposal gave a recall of 0.871, which is equivalent to that with ground truth.

CLAD: a Sensor Management Device forWearable Computing

There has been increasing interest in wearable computing. In wearable computing environments, a wearable computer runs various applications with various sensors (wearable sensors). Since conventional wearable systems do not manage the power supply flexibly, they consume excess power resources for unused sensors. Additionally, sensors frequently become unstable for several reasons such as a breakdown. This instability is hard to detect simply from the sensed data. To solve these problems, we propose a new sensor management device CLAD (cross-linkage for assembled devices) that has various functions for power management and sensed-data management. CLAD improves power saving, data accuracy, and operational reliability.

Multi-algorithm on-site evaluation system for PDR challenge

PDR (Pedestrian Dead Reckoning) is a very promising technology for indoor positioning. We held a technical challenge, entitled the UbiComp/ISWC 2015 PDR Challenge, consisting of the following three categories: a PDR algorithm category; a PDR Evaluation method category; and an exhibition. In this paper, we especially focus on several systems for the PDR algorithm category. A PDR skeleton was prepared for the participants. Using an Android skeleton, participants focus on implementing the PDR algorithm because of the skeletons various functions, such as sensor data acquisition, trajectory visualization, and sensor data upload. The evaluation server evaluates the accuracy of each PDR algorithm automatically as often as sensor data is uploaded to the server and provides a trajectory image file so that participants can compare their PDR algorithms in real time.

Integration of Push-Based and Pull-Based Connectivity Status Sharing for Efficient Data Forwarding towards Mobile Sinks in Wireless Sensor Networks

In this paper, we propose a data forwarding method for efficient data gathering in wireless sensor networks with multiple mobile sinks which freely move in the target region. In our proposed method, each sensor node forwards data packets based on information on connectivity (the status of connection with mobile sinks). As the way to share information on connectivity among neighboring nodes, we integrate push-based and pull-based mechanisms we have proposed in [5]. The integrated mechanism utilizes advertisement from the nodes with good connectivity together with request from the nodes which hold data to transmit. By the complementary use of these mechanisms, our proposed method reduces the traffic for sharing information while realizing an efficient data forwarding.

Evaluation function of sensor position for activity recognition considering wearability

In the wearable computing environment, a computer provides many kinds of services by using the values from wearable sensors to recognize the users movements or situations. In the research on activity recognition, accelerometers are attached on the users body such as wrists, waist and, feet. Though researches on best sensor placement for context aware systems has been released thus far, they do not use enough number of sensors to really decide the best sensor placement. When using these context aware systems in our daily life, we also need to consider the discomfort that the user gets from attaching the sensors. The sensor might get in the users way or feel uncomfortable for the user, however, as far as we know, the sensors wearability is not taken into consideration in these researches. This paper proposes an evaluation function that scores sensor placement considering both recognition accuracy and sensor wearability, with twenty sensors on the users body and thirty kinds of exercises including aerobic exercise, weight training, and yoga. Then we experimentally evaluated sensor placement, resulted in high degree of accuracy achieved without feeling stressful.