Three-axis Accelerometer Sensor Research Articles

Development and Functional Evaluation of an Upper Extremity Rehabilitation System Based on Inertial Sensors and Virtual Reality

An extremity rehabilitation program was proposed based on inertial measurement units (IMU) and virtual reality. A single IMU consists of a three-axis accelerometer, gyroscope, and geomagnetic sensors. One IMU is attached to the upper arm (master) and another to the forearm (slave). The IMUs are connected using a distributed sensor network implemented with interintegrated circuit communication. The motion-tracking algorithm running on a PC tracks the subject's hand based on the estimated IMU orientation and segment lengths through forward kinematics. The training contents, including various dynamic movements and static holds, were designed to evaluate the spatiotemporal aspects of the subject's functionality. The system was tested on a group of healthy subjects and a group with a simulated stiff elbow, allowing the evaluations to be quantitatively differentiated. The stiff elbow was simulated by taping the elbow to restrict the range of elbow motion. We expect the patients to be able to assess their own status without assistance from a therapist and select appropriate training methods to increase their rehabilitation effectiveness. Future studies will verify the availability and reliability of the upper extremity rehabilitation program for patients with a hemiplegia, leading to the development of an upper extremity rehabilitation program for three-dimensional movements of the upper extremities.

An Electronic Circuit for Trickle Charge Harvesting From Littoral Microbial Fuel Cells

In this paper, the design of an electronic circuit for harvesting energy trickling from benthic sources and the long-term performance in powering sensors and devices in a littoral tidal basin are considered. The process has to contend with the randomness, diurnal variations, and low levels and voltages in available energy compared to what is required by oceanographic sensors. The system has two components: a circuit for conditioning the power and a large area electrode array in a littoral basin. The circuit has two stages and is able to overcome the leaks in capacitors used to store the power; the energy is stored first in a small capacitor and then in a large one so that voltages compatible with sensors are produced. The footprint of the anode is 10 m2 and is inserted into the sediment and the cathode resides in the salt water above the sediment. Several different sensors and actuators have been autonomously powered by the stored energy (4.2 kJ at 12 V). A beacon and an underwater acoustic sensor (5 h of activation of combined hydrophone and three-axis accelerometer sensors, every 40 h; duty cycle has been doubled with recent improvements) have been powered over one to two months. Also, in-water propulsion (for 165 s at a time) of a 25-W biorobotic flapping fin propulsor has been achieved. The system is suitable primarily for powering sensors. It has been operated for more than three years at useful duty cycles, indicating sustainability for autonomous usage.

가속도센서와 각속도센서를 이용한 특정 비정상보행에 관한 연구

Recently, technologies to help the elderly or disabled people who have difficulty in walking are being developed. In order to develop these technologies, it is necessary to construct a system that gathers the gait data of people and analysis of these data is also important. In this research, we constructed the development of sensor system which consists of pressure sensor, three-axis accelerometer and two-axis gyro sensor. We used k-means clustering algorithm to classify the data for characterization, and then calculated the symmetry index with histogram which was produced from each cluster. We collected gait data from sensors attached on two subjects. The experiment was conducted for two kinds of gait status. One is walking with normal gait; the other is walking with abnormal gait (abnormal gait means that the subject walks by dragging the right leg intentionally). With the result from the analysis of acceleration component, we were able to confirm that the analysis technique of this data could be used to determine gait symmetry. In addition, by adding gyro components in the analysis, we could find that the symmetry index was appropriate to express symmetry better.

Ship Detection with Wireless Sensor Networks

Surveillance is a critical problem for harbor protection, border control or the security of commercial facilities. The effective protection of vast near-coast sea surfaces and busy harbor areas from intrusions of unauthorized marine vessels, such as pirates smugglers or, illegal fishermen is particularly challenging. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental Wireless Sensor Network (WSN) on the sea's surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect any passing ships by distinguishing the ship-generated waves from the ocean waves. We design a three-tier intrusion detection system with which we propose to exploit spatial and temporal correlations of an intrusion to increase detection reliability. We conduct evaluations with real data collected in our initial experiments, and provide quantitative analysis of the detection system, such as the successful detection ratio, detection latency, and an estimation of an intruding vessel's velocity.

Toothbrushing Region Detection Using Three-Axis Accelerometer and Magnetic Sensor

Due to the possible occurrence of periodontal disease at an early age, it is important to have proper toothbrushing habits as early as possible. With this aim, the feasibility and concept of a smart toothbrush (ST) capable of tracing toothbrushing motion and orientation information was suggested. In this study, we proposed the advanced ST system and brushing region classification algorithm. In order to trace the brushing region and the orientation of a toothbrush in the mouth, we required the absolute coordinate information of ST. By using tilt-compensated azimuth (heading) algorithm, we found the inclination and orientation information of the toothbrush, and the orientation information while brushing inner tooth surfaces showed specific heading features that could be reliably discriminated from other brushing patterns. In order to evaluate the feasibility of clinical usage of the proposed ST, 16 brushing regions were investigated by 15 individual healthy subjects. The proposed ST system demonstrated 97.1%(±0.91) of the region detection accuracy and 15 brushing regions could be classified. This study also showed that the proposed ST system may be helpful for dental care personnel in patient education and instruction for oral hygiene regarding brushing habits.

A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors

This paper presents a framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors. In our framework, the start and end points of meaningful gesture segments are detected automatically by the intensity of the EMG signals. A decision tree and multistream hidden Markov models are utilized as decision-level fusion to get the final results. For sign language recognition (SLR), experimental results on the classification of 72 Chinese Sign Language (CSL) words demonstrate the complementary functionality of the ACC and EMG sensors and the effectiveness of our framework. Additionally, the recognition of 40 CSL sentences is implemented to evaluate our framework for continuous SLR. For gesture-based control, a real-time interactive system is built as a virtual Rubik's cube game using 18 kinds of hand gestures as control commands. While ten subjects play the game, the performance is also examined in user-specific and user-independent classification. Our proposed framework facilitates intelligent and natural control in gesture-based interaction.

Early recognition of upper limb motor tasks through accelerometers: real-time implementation of a DTW-based algorithm

A new real-time implementation of a Dynamic Time Warping (DTW)-based classification scheme is presented here, and its performance evaluated on experimental data. Nine young adults were requested to perform instances of eight different purposeful movements described in the Wolf Motor Function Test, while wearing a three-axis accelerometer sensor placed on the inner forearm. Results include the correct recognition percentage, as compared to a classification scheme based on the traditional DTW measure, and the recognition percentage as a function of the time elapsed from the beginning of the performed movements. The Real-Time DTW basically performs with the same accuracy of the traditional DTW-based classification scheme (91.5% of correct recognition percentage), a figure that increases to 96.5% if the multidimensional scheme is adopted. Moreover, more than 60% of movements are correctly recognized before their end, thus setting the way for applications in rehabilitation and assistive technologies, where a real-time control scheme is able to interact with the user while the movement is being performed.

A Physics Road Rally

Our school recently acquired1 Vernier's Wireless Dynamics Sensor System (WDSS). The WDSS consists of a three-axis accelerometer, altimeter, and force sensor that has the ability to remotely collect data for later transfer to a computer.2 While our primary purpose for acquiring the WDSS was to enhance our amusement park physics experiments, we decided to test one of the WDSS units by using it to collect data during the drive home from school one day, since measuring the acceleration of a car is one of the experiments suggested in the Vernier Wireless Dynamics Sensor System User Manual. We taped the device to the floor of the car with its +x-axis pointed forward and its +y-axis pointed to the left. The WDSS was configured to take five samples per second for a total time period of 10 minutes.