An infusion system for infusing a liquid into a body includes an external infusion device and a remote commander. The external infusion device includes a housing, a receiver, a processor and an indication device. The receiver is coupled to the housing and for receiving remotely generated commands. The processor is coupled to the housing and the receiver to receive remotely generated commands and to control the external infusion device in accordance with the commands. The indication device indicates when a command has been received and indicates when the command is being utilized to control the external infusion device so that the external infusion device is capable of being concealed from view when being remotely commanded. The remote commander includes a commander housing, a keypad for transmitting commands, and a transmitter for transmitting commands to the receiver of the external infusion device.

External infusion device with remote programming, bolus estimator and/or vibration alarm capabilities

This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.

The extraction of neural strategies from the surface EMG

A monitoring system includes one or more wireless nodes forming a wireless mesh network; a user activity sensor including a wireless mesh transceiver adapted to communicate with the one or more wireless nodes using the wireless mesh network; and a digital monitoring agent coupled to the wireless transceiver through the wireless mesh network to request assistance from a third party based on the user activity sensor.

Mesh network personal emergency response appliance

A heart monitoring system for a person includes one or more wireless nodes; and a wearable appliance in communication with the one or more wireless nodes, the appliance monitoring vital signs.

Health monitoring appliance

A health care monitoring system for a person includes one or more wireless nodes forming a wireless mesh network; a wearable appliance having a sound transducer coupled to the wireless transceiver; and a heart attack or stroke attack sensor coupled to the wireless mesh network to communicate patient data over the wireless mesh network to detect a heart attack or a stroke attack.

Mesh network stroke monitoring appliance

http://www.bu.edu/nmrc/files/2010/06/103.pdf

Filtering the surface EMG signal: Movement artifact and baseline noise contamination

This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to >97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically ≤5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.

Decomposition of Surface EMG Signals

A biosignal monitoring system including a plurality of sensors for disposition in predetermined positions on the body of a test subject; each sensor having contact surfaces shaped and arranged to detect a particular biosignal generated in the body, a sensor transceiver, a sensor antenna, a voltage supply, and a microprocessor programmed for processing the particular biosignal to provide given data; and a control station providing a wireless, bi-directional data communications link with the sensors; the control station having a station transceiver, a station antenna, and a computer for further processing the given data received from the sensors.

Biosignal monitoring system and method

http://www.bu.edu/nmrc/files/2012/01/108.pdf

Inter-electrode spacing of surface EMG sensors: reduction of crosstalk contamination during voluntary contractions.

A device and a method for anatomic implantation to provide electrical interface to nerves. Included in the device are a pair of electrical leads secured to a coupling formed from a piece of woven sheet material, and having ends connected to spaced apart input terminals disposed adjacent to an inner surface of the coupling. Connected to the opposite ends of the leads are output terminals for interfacing to electrical recording or measuring instruments. During implantation, a subject nerve is embraced by the inner surface of the sheet material which is shaped for juxtaposition the outer surface of the nerve at a minimum spacing therefrom of 0.3 millimeters. In the period following implantation the interstices of the sheet material coupling receive anatomic tissue growth establishing a structurally stable dimensional relationship between the nerve and both the input electrodes and the coupling. The fixed input electrode spacing between the electrode and the nerve improves the quality of the electrical signals received from the nerve while the 0.3 millimeter minimum spacing insures that the nerve will continue living for extended periods of time.

L. Donald Gilmore

Papers

Filtering the surface EMG signal: Movement artifact and baseline noise contamination

Decomposition of Surface EMG Signals

Biosignal monitoring system and method

Inter-electrode spacing of surface EMG sensors: reduction of crosstalk contamination during voluntary contractions.

Method and apparatus for interfacing to nerves