A metabolic energy monitoring system having one or more physiological measurement platforms and displays enabling the calculation and display of energy balance, kilocalorie energy expenditure and kilocalorie intake is described. In preferred embodiments, the system utilizes one or more on-body monitoring platforms to enable measurement of change in body composition and kilocalorie energy expenditure over a period of time thereby enabling a comparator to calculate net energy balance over this period of time and to calculate kilocalorie intake over this same period of time. Such data may then be displayed on a display device in wireless communication with the on-body monitoring platform to provide the user of the system with useful information and guidance in weight management applications.

In a method and apparatus for swallowing impairment detection, a candidate executes one or more swallowing events, and dual axis accelerometry data is acquired representative thereof. Upon feature extraction and classification, vibrational data acquired in respect of each swallowing event is classified as indicative of one of normal or possibly impaired swallowing.

Remote monitoring of a subject wearing a sports helmet is enabled. In one aspect, an example system includes a safety helmet and a sensor integrated with the helmet for continuously gathering head acceleration force data, the head acceleration force data associated with the head movements of a subject. The system can also include a wireless transceiver coupled to the sensor for transmitting the head acceleration force data and a mobile device for receiving the head acceleration force data from the wireless transceiver. The system can further include a database engine for displaying the head acceleration force data to a user.

Remote monitoring of a subject wearing a sports helmet is enabled. In one aspect, an example system includes a safety helmet and a sensor integrated with the helmet for continuously gathering head acceleration force data, the head acceleration force data associated with the head movements of a subject. The system can also include a wireless transceiver coupled to the sensor for transmitting the head acceleration force data and a mobile device for receiving the head acceleration force data from the wireless transceiver. The system can further include a database engine for displaying the head acceleration force data to a user.

There is set forth herein a system including a camera device. In one embodiment the system is operative to perform image processing for detection of an event involving a human subject. There is set forth herein in one embodiment, a camera equipped system employed for fall detection.

A system is disclosed herein for providing a kinetic assessment and preparation of a prosthetic joint comprising one or more prosthetic components. The system comprises a prosthetic component including sensors and circuitry configured to measure load, position of load, and joint alignment. The system further includes a remote system for receiving, processing, and displaying quantitative measurements from the sensors. The kinetic assessment measures joint alignment under loading that will be similar to that of a final joint installation. The kinetic assessment can use trial or permanent prosthetic components. Furthermore, adjustments can be made to the applied load magnitude, position of load, and joint alignment by various means to fine-tune an installation. The kinetic assessment increases both performance and reliability of the installed joint by reducing error that is introduced by elements that load or modify the joint dynamics not taken into account by prior assessment methods.

Disclosed is a capacitive sensor (100) for locating the presence of an individual and/or of an object, the sensor (100) including:—a first layer (C1) including at least one first electrode (E1i, i∈[1,N]) extending in a first direction (d1);—a second layer (C2) having at least one second electrode (E2j, j∈[1,M]) extending in a second direction (d2); in which the first direction (d1) is different from the second direction (d2), and in which the first layer (C1) is electrically insulated from the second layer (C2).

A method for tracking the position of a person in an environment, such as a medical room, relative to a medical device is presented. The method comprises identifying the position of a medical device in the environment, providing a mobile device in the vicinity of a person in the environment, the mobile device configured to follow movement of the person in the environment, and identifying the position of the mobile device in the environment by using at least one stationary device. Further, the method comprises using a processor to process data relating to the position of the mobile device in the environment to obtain an expected position of the person in the environment, data relating to the position of the medical device in the environment, and data relating the expected position of the person in the environment to obtain an expected relative position of the person with respect to the medical device.

Various embodiments described herein relate to a method and related wearable device and non-transitory machine-readable medium including receiving sensor data from at least one sensor of the wearable device; comparing the sensor data to schedule format stored in a memory of the wearable device, wherein the schedule format specifies at least one characteristic of sensor readings previously associated with a predefined context; determining that the received sensor data matches the schedule format; determining, based on the received sensor data matching the schedule format, that the user is currently in the predefined context; identifying an action associated with the predefined context; and executing the action while the user is in the predefined con text.

A system uses a wearable electronic device to monitoring behavior of an operator of a vehicle. The wearable device is worn on the head of the operator of the vehicle and includes one or more motion sensors. A processor of the wearable device or of a proximate portable electronic device receives data from the one or more motion sensors and detects a pattern of motion based on the data. The system then may determine a physiological state or physical behavior of the driver based on the detected patterns or motion. Detected physiological states may include head bobs associated with sleepiness. Detected physical behaviors may include dangerous behaviors such as phone usage, prolonged gazes, sudden lane changes, or hard braking or steering.