Body sensor networks to evaluate standing balance (interpreting muscular activities based on inertial sensors) (original) (raw)
Related papers
Body sensor networks to evaluate standing balance
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments - HealthNet '08, 2008
In this paper, we present a system that integrates inertial sensors and electromyogram (EMG) signals, which measures the muscular activities while performing motions. The objective of our study is to investigate the behaviour of the EMG signals to interpret the activity of standing balance. Quantitative parameters for balance are obtained from an inertial sensor through a bodysensor network. These parameters are further used to find the prominent features in the EMG signal. The inertial sensor used in this system is an accelerometer. The implementation details and effectiveness of using EMG signals are also provided.
IEEE Transactions on Information Technology in Biomedicine, 2000
The evaluation of the Postural Control System (PCS) has applications in rehabilitation, sports medicine, gait analysis, fall detection, and diagnosis of many diseases associated with a reduction in balance ability. Standing involves significant muscle use to maintain balance, making standing balance a good indicator of the health of the PCS. Inertial sensor systems have been used to quantify standing balance by assessing displacement of the Center Of Mass (COM), resulting in several standardized measures. Electromyogram (EMG) sensors directly measure the muscle control signals. Despite strong evidence of the potential of muscle activity for balance evaluation, less study has been done on extracting unique features from EMG data that express balance abnormalities. In this paper, we present machine learning and statistical techniques to extract parameters from EMG sensors placed on the Tibialis anterior and Gastrocnemius muscles which show a strong correlation to the standard parameters extracted from accelerometer data. This novel interpretation of the neuromuscular system provides a unique method of assessing human balance based on EMG signals. In order to verify the effectiveness of the introduced features in measuring postural sway, we conduct several classification tests that operate on the EMG features and predict significance of different balance measures.
Posture Determination Using a Body Sensor Network
Cogent Computing ARC internal report (COG.006), 2008
Due of the large number of degrees of freedom of the human body, posture monitoring of human during activity regimes presents many research challenges. Several research groups world wide have engaged with the development of low-power wireless body sensor networks that are capable of providing real-time posture tracking for a variety of applications, such as dance and sports. The work reported here is concerned with the development of a wireless body sensor network that, as opposed to posture tracking, can: a) provide the identification and classification of eight human postures (standing, kneeling, sitting, crawling, walking, laying down on front and back, and laying on one side) in real-time and b) is able to relate this information wirelessly to a remote monitoring point. Posture information is an essential part of monitoring operatives in safety critical missions. The work sits within a larger project aiming to increase general safety of operatives in bomb disposal missions.
Wearable sensors network for activity recognition using inertial sensors
2015
The aim of this paper is to present the implementation of an activity recognition system based on a data acquisition system with inertial sensors. One of the requirement of data acquisition systems used in human activity recognition is their level of acceptability or their level of obstruction [2, 4]. Starting from this important requirement, we designed a few wearable data acquisition systems using inertial sensors. The idea from which we started, takes into account the need to obtain more complete information about the studied human subject, in complex activities recognition systems. In our experiment we used inertial sensor built into so called “9DOF inertial module”. Our systems contain, on the wearable side, a maximum of three inertial sensors, a microcontroller module, and a communication module which can be one of the three solutions. For data reception, on the fixed part of the system, we have a communication module corresponding to the one on wearable parts and a microcontroller module connected to a PC through a serial port. For activity recognition, data files are transferred to a PC, and data is processed in Matlab using artificial neural networks as classifier. The networks have been configured according to the number of sensors that acquired data and the number of activities that we want to recognize.
Wearable Sensor System for Monitoring Body Kinematics
— Existing human body motion capture solutions rely on camera based systems limited to confined measurements, or Inertial Measurement Units (IMUs) prone to noise and drift, resulting in position inaccuracies. This investigation demonstrates a proof-of-concept wearable sensor system which accurately monitors human body kinematics in real-time using Radio Frequency (RF) positioning sensors combined with MEMS based IMU sensors. In certain IMU orientations, we measured an average pitch error of < 2 degrees for the combined method, compared with 12 degrees for an IMU alone. This self-contained sensor network has applications including military training, gaming, sports and healthcare.
Inertial Sensor Based Identification of Human Movement, BIODEVICES 2009 Proceedings
… Conference on Biomedical Electronics and Devices, …, 2009
The scope of this paper is the presentation of experiments which involve measurements and identification of human movements by using the inertial sensors. We describe the purpose, design and obtained results of two experiments, as well as our future plans which include the exploration of the forces acting at spine segments by measurements with inertial sensors. The first experiment implemented the method for measuring the range of motion (RoM) of head in transverse plane . It was done in the Laboratory of Biomechanics and Automatic Control -LaBACS, University of Split. In the second experiment we analyzed the standing -up movement and we used the robot assistive device for the support of human while performing the standing -up task. Measurements for purposes of this experiment were done in the Laboratory of Biomedical Engineering and Robotics, University of Ljubljana. We have proposed the new method which uses the Extended Kalman filtering for combining the data acquired from inertial sensor measurements of standing -up movement with data from the dynamic human body model . Our plans regarding the next experiment are focused on the identification of the spinal load during sitting and standing, by using the inertial sensors measurement system.
Observing the State of Balance with a Single Upper-Body Sensor
Frontiers in Robotics and AI, 2016
The occurrence of falls is an urgent challenge in our aging society. For wearable devices that actively prevent falls or mitigate their consequences, a critical prerequisite is knowledge on the user's current state of balance. To keep such wearable systems practical and to achieve high acceptance, only very limited sensor instrumentation is possible, often restricted to inertial measurement units at waist level. We propose to augment this limited sensor information by combining it with additional knowledge on human gait, in the form of an observer concept. The observer contains a combination of validated concepts to model human gait: a spring-loaded inverted pendulum model with articulated upper body, where foot placement and stance leg are controlled via the extrapolated center of mass (XCoM) and the virtual pivot point (VPP), respectively. State estimation is performed via an Additive Unscented Kalman Filter (Additive UKF). We investigated sensitivity of the proposed concept to model uncertainties, and we evaluated observer performance with real data from human subjects walking on a treadmill. Data were collected from an Inertial Measurement Unit (IMU) placed near the subject's center of mass (CoM), and observer estimates were compared to the ground truth as obtained via infrared motion capture. We found that the root mean squared deviation did not exceed 13 cm on position, 22 cm/s on velocity (0.56-1.35 m/s), 1.2°on orientation, and 17°/s on angular velocity.
Journal of Systems Architecture, 2011
Human body movement can be monitored through a wireless network composed of inertial sensors. This work presents the development of Wagyromag (Wireless Accelerometer, GYROscope and MAGnetometer), a wireless Inertial Measurement Unit (IMU) composed of a triaxial accelerometer, gyroscope and magnetometer. Communication is based on a 802.15.4 network. Furthermore, calibration, signal conditioning and signal processing algorithms are presented throughout this work. Wagyromag's high potential permits its application in a wide range of medical applications such as telerehabilitation, nocturnal epilepsy seizure detection, fall detection and other applications in the field of sport science.
Original Research Effect of the Placement of the Inertial Sensor on the Human Motion Detection
There are numerous possibilities of assessments of the human activity, offered by the ActimedARM-a wearable inertial sensor we developed. This device features a triaxial magnetometer, a trixial accelerometer, a micro-processing unit, a Zigbee module and a μSD card. Its embedded algorithms make it able to compute postures, transfers of the subject and also to characterize the walking episodes. We recently succeeded in computing the relative displacements of the sensors, from double integration of the acceleration signals, in order to qualify specific physical activities such as rising from chairs or stools. The experiments highlighted the impact of the location of the sensor on the body on the correlation between objective motion and signals processed from acceleration measurements, showing a better correlation coefficient of 11.41% when the sensor is located on the navel.
2007
This paper presents a study on quantitative dynamics analysis of human lower limb using developed wearable sensor systems that can measure reaction force and detect the following gait phases: initial contact, loading response, mid stance, terminal stance, pre-swing, initial swing, mid swing and terminal swing. Since conventional camera-based motion analysis system and reaction force plate system require costly devices, vast space as well as time-consuming calibration experiments, the wearable sensor-based system is much cheaper. Gyroscopes and two-axis accelerometers are incorporated in this wearable sensor system. The former are attached on the surface of the foot, shank and thigh to measure the angular velocity of each segment, and the latter are used to measure inclination of the attached leg segment (shank) in every single human motion cycle for recalibration. Ground reaction forces during human walking are synchronously measured using a wearable force sensor integrated in a shoes mechanism. Finally, experiment has been performed to compare the measurement results from the wearable sensor system with the data obtained from an optical motion analysis system and a force plate. The results showed that the measurement of human lower limb orientations and reaction forces for human dynamics analysis could be reliably implemented using the wearable sensor systems.