少子高齢化,医療費高騰の問題に限らず,我々の身体的?精神的な健康を維持?回復することは自立した人間として生きていく上でもっとも中国足球彩票なことである. そのためには,人の身体的?精神的な状態を,常に生活に不便?悪影響の無い方法で計測し,そのモデル化?解析結果に基づいて,健康?正常な状態を維持?回復するための方法を人に提案していくための研究を進める. そのための広範囲に渡る研究テーマを,特に大量?多人数?長期間のデータからの人のモデル化(身体モデル,動きモデル,精神モデル,脳モデル,など)を取り扱う. 主に,
過去?現在の具体的な研究テーマを以下に示す.
Our goal is to develop a system for coaching human motions
(e.g. rehabilitation). Common motion measurement systems are too
expensive and require users to wear binding devices. The proposed
system utilizes an inexpensive depth-measurement sensor
(i.e. Microsoft Kinect). in order to get high-measurement accuracy
with no body-equipped devices. The system functionally consists of
three modules below. The first one estimates the sequence of a body
pose from a depth image sequence captured while a user performs a
target motion. The second one evaluates the gaps between the
sequences of the estimated poses and a good template. The third one
coaches the user on how to modify his/her motion so that it gets
closer to the good template. This work focuses on achieving the third
point. To this end, it is important to efficiently advise the user to
emulate the crucial features that define the good template. This is
because many other features of the target motion might be varied among
individuals, but those variations give less impacts on evaluating the
target motion. The proposed method automatically mines the crucial
features of any kind of motions from a set of all motion features. The
crucial features are mined based on feature sparsification through
binary classification between the samples of good and other motions.
Experimental results demonstrated that 1) the proposed method could
extract intuitively-correct crucial features (as shown
in Fig. 1) and 2) the extracted features improved
the accuracy in classifying good and other motions.
The development of a widely applicable automatic motion coaching
system requires to address a lot of issues including motion capturing,
motion analysis and comparison, error detection as well as error
feedback. In order to cope with this complexity, most existing
approaches focus on a specific motion sequence or exercise. As a first
step for the development of a more generic system, this work
systematically analyzes different error and feedback types. A
prototype of a feedback system that addresses multiple modalities is
presented. The system allows to evaluate the applicability of the
proposed feedback techniques for arbitrary types of motions in a next
step. The screenshot of the developed interface system is shown in
Figure 2.
This work discusses the usefulness of human body-parts tracking for
acquiring subtle cues in social interactions. While many kinds of
body-parts tracking algorithms have been proposed, we focus on
particle filtering-based tracking using prior models, which have
several advantages for researches on social interactions. As a first
step for extracting subtle cues from videos of social interaction
behaviors, the advantages, disadvantages, and prospective properties
of the body-parts tracking using prior models are summarized with
actual results.
?身体動作修正のための人体計測?可視化