Health and sport applications have been a flourishing area for deploying Wireless Body Area Networks (WBAN) as this technology can provide a real time feedback which is important for the user, coaches, doctors and the viewing public. A wireless accelerometer sensor module was used to determine the link performance by recording the data and traffic lost on different runners and for different transmitter locations around the human body (foot, leg and arm). An approximate swing time calculation algorithm was employed to find the swing time effect on these losses. Experimental measurements showed 98% reliability at 250kbps while 62% resulted when using a data rate of 2Mbps. The results also showed that the sensor on the wrist gives the best outcome from the locations tested.
This paper investigates deteriorations in knee and ankle dynamics during running. Changes in lower limb accelerations are analyzed by a wearable musculoskeletal monitoring system. The system employs a machine-learning technique to classify joint stiffness. A maximum-entropy-rate method is developed to select the most relevant features. Experimental results demonstrate that distance travelled and energy expended can be estimated from observed changes in knee and ankle motions during 5-km runs.
Health and sport applications have been a flourishing area for deploying Wireless Body Area Networks (WBAN) as this technology can provide a real time feedback which is important for the user, coaches, doctors and the viewing public. This paper presents the wireless channel reliability and efficiency for time multiplexing and star shape body area network operating on 2.45GHz with a data rate of 1Mbps. The network employs sensor locations on both arms and legs of the human participant to sense and send acceleration data during the course of running. The results show a tradeoff between the channel occupancy and traffic generated to provide high channel reliability for the body network.
A time division multiplexed coding system was designed and tested for a body-centric star network using a wireless node mounted in six different places in different time lots on the arm and leg and a central node (hub) on the chest during running. The proposed network allows a coordinated approach to gait analysis. The time sequence for communications between the wireless nodes and the hub is set during the first few steps as runners have different styles and can run at different speeds. After calibration the central unit sends a synchronization pulse during every running step and sets a unique transmission time window for each individual node. The time windows are scheduled when there is reliable communications between the hub and the sensor nodes around the body. Accelerometers on each node are used to identify these time windows for the diverse angles of rotation of the human limbs during running.
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