A MAC protocol with high scalability for motion capture based on frequency division multiple and time division multiple access.

A good performance of motion capture, which belongs to body sensor network, depends on a reasonable design of MAC protocol. The purpose of this study is to design a reliable and highly extensible protocol for applying in motion capture. The proposed MAC protocol can easily be actualized by the timer in the chip. With this MAC protocol, the network would be built quickly. One or more nodes could be added easily in the net or deleted randomly from the net. In order to verify the superiority of this protocol, a series of experiments were designed. The results showed that the mean of simulation receive frames for node1-node7 from each stage were very close to the original frames. In addition, the final Pocket Loss Rates for node1-node7 were 0.081%, 0.175%, 0.143%, 0.249%, 0.248%, 0.044% and 1.897%, which could be in the error-allowed range. Thus, this protocol is stable and reliable, which can be widely used to capture human movement signal.

A wearable action recognition system based on acceleration and attitude angles using real-time detection algorithm.

Falls are a main cause of trauma and death. The purpose of this study is to adopt unique resultant acceleration and attitude angles to distinguish falls from activities of daily life before impact. In this study, we developed a wearable action recognition system to acquire action data. The moving average filter was employed to deal with raw data, and then complementary filter was adopted to compromise sensor data for attitude angles. The real-time detection algorithm embedded in this device was applied to recognize six actions based on processed data. Eight subjects (five males, three females) participated in the experiment. The optimal features and related thresholds were extracted. In addition, the real-time action detection results indicated that the real-time action recognition model reached an accuracy of 96.25%, with 98% for male and 93.3% for female. Thus, our device potentially achieves a high sensitivity of fall-related actions recognition.

Relationship between dynamical characteristics of sit-to-walk motion and physical functions of elderly humans.

Sit-to-walk (STW) motion is essential for daily activities. Falls frequently occur, when there is impaired ability to perform STW movements. This study investigated the relationships between the dynamical characteristics of STW motion and physical functions of elderly people. 128 elderly (51 males and 77 females, above 65 years) participated in this study. Participants were instructed to perform STW motion at comfortable state and classified into four groups (normal, mild, moderate and severe group) based on physical function, which evaluated with functional reach test and dynamic gait test. The results showed that some relationships were confirmed between the sample entropy characteristics of STW motion. Moreover, a subset of variables was significant different among four groups via Kruskal-Wallis test, which could be potentially used for developing an objective and simple methods to assess balance capacity and fall risk level of elderly people.

Fall risk factors analysis based on sample entropy of plantar kinematic signal during stance phase.

Falls are a multi-causal phenomenon with a complex interaction. The aim of our research is to study the effect of multiple variables for potential risk of falls and construct an elderly fall risk assessment model based on demographics data and gait characteristics. A total of 101 subjects, whom belong to Malianwa Street, aged above 50 years old and participated in questionnaire survey. Participants were classified into three groups (high, medium and low risk group) according to the score of elderly fall risk assessment scale. In addition, the data of ground reaction force (GRF) and ground reaction moment (GRM) was record when they performed walking at comfortable state. The demographic variables, sample entropy of GRF and GRM, and impulse difference of bilateral foot were considered as potential explanatory variables of risk assessment model. Firstly, we investigated whether different groups could present difference in every variable. Statistical differences were found for the following variables: age (p=2.28e-05); impulse difference (p=0.02036); sample entropy of GRF in vertical direction (p=0.0144); sample entropy of GRM in anterior-posterior direction (p=0.0387). Finally, the multiple regression analysis results indicated that age, impulse difference and sample entropy of resultant GRM could identify individuals who had different levels of fall risk. Therefore, those results could potentially be useful in the fall risk assessment and monitor the state of physical function in elderly population.