A motion monitoring method (500) is provided, which includes: obtaining a movement signal of a user during motion, wherein the movement signal includes at least an electromyographic signal or an attitude signal (510); and monitoring a movement of the user during motion based at least on feature information corresponding to the electromyographic signal or the feature information corresponding to the attitude signal (520).

A fitness tracking system includes a shoe, a monitoring device, and a controller. The monitoring device is mounted on the shoe and includes an accelerometer configured to generate acceleration data corresponding to acceleration of a foot received by the shoe. The controller is operably connected to the accelerometer and is configured to collect sampled acceleration data by sampling the generated acceleration data, to identify foot strike data of the sampled acceleration data, to identify a local minimum of the sampled acceleration data collected prior to the foot strike data, and to determine foot strike characteristic data corresponding to the foot strike data based on an acceleration value at the local minimum.

A system and a method are described for monitoring a medical care environment. In one or more implementations, a method includes identifying a first subset of pixels within a field of view of a camera as representing a bed. The method also includes identifying a second subset of pixels within the field of view of the camera as representing an object (e.g., a subject, such as a patient, medical personnel; bed; chair; patient tray; medical equipment; etc.) proximal to the bed. The method also includes determining an orientation of the object within the bed.

The disclosure provides a dynamic interaction-oriented subject's limb time-varying stiffness identification method and device. The method includes: the combination of subject's limb displacement and measured force data or the combination of angle and measured torque data is collected; based on the time-varying dynamic system constructed based on a second-order impedance model, the linear parameter varying method is utilized to substitute the time-varying impedance parameters and reconstruct the restoring force/torque expression; iterative identification is performed on variable weights, dynamic interaction force/torque, and restoring force/torque by using time-varying dynamic parameters based on the dynamic interaction force/torque expression expanded from basis function; the time-varying stiffness is solved by using variable weights and dynamic interaction force/torque according to expression with substituted the time-varying impedance parameters. The disclosure not only improves the accuracy of the time-varying stiffness identification technology but also expands the application scenarios of the time-varying stiffness identification technology.

Ergonomics improvement systems having wearable sensors and related methods. An example ergonomics improvement system includes an encoder system to couple to a limb of a body. The encoder sensor system to generate first outputs in response to movement of the limb relative to the body to determine a position of the limb relative to the body. The system includes a load sensor to generate a second output representative of a load carried by the body and a position sensor to generate a third output representative of a position of a right foot of the body relative to a position of a left foot of the body.

An orthopedic system configured for use in a pre-operative, intra-operative, and post-operative assessment. The orthopedic system comprises a first screw, a second screw, a first device, a second device, and a computer. The first device and the second device are respectively coupled to a first bone and a second bone of a musculoskeletal system. The first and second devices each include electronic circuitry, one or more sensors, and an IMU. A bracket, wrap, or sleeve can be used to hold the first and second devices to the musculoskeletal system. The first and second devices are configured to send measurement data to a computer. The first and second devices each have an antenna system. Electronic circuitry in the first or second devices are configured to harvest energy from a received radio frequency signal to recharge a battery to maintain operation.

A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.

An energy balancing system scales portion sizes of user-selected meal recipes to control a digital scale to prompt the user to measure scaled quantities of meal ingredients to prepare scaled meals. Physical activity tracking devices provide data to generate physical activity energy expenditures for energy burned by physical activity. The physical activity energy is added to a basal metabolic rate of energy expenditure that is a function of sex, weight, height, and age. The total energy expended are scaled down for a weight-loss goal to obtain the total recommended energy. A recipe portion-size optimizer adjusts scaling factors for each recipe so that the total recommended energy is met by the scaled meals. Amounts of nutrients for the recipes can also be scaled by the scaling factors to match recommended nutrient amounts when the meal recipe optimizer generates the scaling factors using an over-determined linear system optimizer.

For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

A method for determining a position variation of an eyewear equipment worn by a wearer includes obtaining reference data representing a reference position of an eyewear equipment worn by the wearer, the eyewear equipment including at least one ophthalmic lens and a frame on which the at least one ophthalmic lens is mounted, determining at least one reference position parameter indicative of a reference position of the eyewear equipment on the face of the wearer based on the reference data, obtaining current data representing a current position of the eyewear equipment worn by the wearer, determining at least one current position parameter indicative of a current position of the eyewear equipment in front of the eyes of the wearer based on the current data, and comparing the reference and current positions parameters to determine a position variation of the eyewear equipment worn by the wearer.