Systems and methods for positioning one or more sensors on a user. The system has user sensors, apparatus sensors, and treatment sensors. A processing device, executing computer readable instructions stored in a memory, cause the processing device to: generate an enhanced environment representative of an environment; receive apparatus data representative of a location of the apparatus in the environment; generate an apparatus avatar in the enhanced environment; receive user data representative of a location of the user in the environment; generate a user avatar in the enhanced environment; receive treatment data representative of one or more locations of the treatment sensors in the environment; generate, treatment sensor avatars in the enhanced environment; calculate a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user; and generate instruction data representing an instruction for positioning the treatment sensors at the treatment location.

The invention relates to an interactive training module, comprising at least one image-displaying wall (1, 2) with which motion sequences and/or objects can be displayed to a user, at least one position sensing device (7) with which the positions of at least the user's hands can be detected, and a control unit with which these positions can be compared with those which are stored in the control unit, wherein the control unit can display correction information to the user by means of the at least one image-displaying wall (1, 2) on the basis of the difference between the stored position and the detected position.

Systems and methods for facilitating exercise monitoring are provided. A representative method includes: using a processor, having processor circuitry, to: obtain motion readings reported from an inertial sensor of a wearable device and corresponding to a scheduled training types; receive the schedule and a target status of a target training count or a target training time corresponding to each of the training types; determine the current training type based on the schedule; select one of the computing models based on the current training type; compare the set of matching characteristics corresponding to the current training type and the motion readings until the target status has been attained for the current training type; select a next training type based on the schedule; select another one of the computing models based on the next training type; determine activity information associated with the schedule; and compute an exercise amount of the user based on the target status, the activity information, and the schedule.

In one aspect, the present disclosure relates to a method including obtaining a plurality of heart rate measurements of the user over a period of time; obtaining motion data of the user over the period of time; analyzing the motion data of the user to determine for each of the plurality of heart rate measurements, a corresponding work rate measurement; determining, for each of the plurality of heart rate measurements, a first confidence level; determining, for each corresponding work rate measurement, a second confidence level; and estimating a first energy expenditure rate using the plurality of heart rate measurements; estimating a second energy expenditure rate using the plurality of work rate measurements; and estimating a weighted energy expenditure rate of the user by combining the first energy expenditure rate weighted by the first confidence level and the second energy expenditure rate weighted by the second confidence level.

A muscle assessment protocol can include: attaching one or more surface electromyometry (sEMG) sensors to the skin of a subject to be operably coupled with one or more muscles; operably coupling the one or more sEMG sensors to a computing system; performing the predetermined muscle activity of a muscle assessment protocol that includes a frequency-based, amplitude-adjusted root mean square protocol; monitoring/recording sEMG data of the one or more muscles during the predetermined muscle activity; and providing the sEMG data to the subject such that the subject can improve muscle performance for the predetermined muscle activity by using the sEMG data. The muscle activity includes static or dynamic muscle use. The predetermined muscle activity can be provided to the subject by the computing system.

A room or area designed to facilitate a plurality of users moving while they learn, work, or participate in a simulation. sensor relays aid this process by sending and receiving user information to a central hub, a movement device, one another or any combination of the three, in order to safely benefit the users coordination, exercise, or concentration while multitasking, according to their own user defined set points, a set of default set points, or set points determined by an external observer. This room or area may be further enhanced in applications where a treadmill or moving walkway is present.

Embodiments are related to systems and methods for data determining information about a subject, and more particularly to systems and methods for utilizing and distributing information related to measurements of a human subject.

According to one embodiment, an apparatus comprising a portable monitoring device to be affixed to a user. The portable monitoring device including: 1) a set of one or more sensors to generate sensor data indicative of physical activity of a user when the portable monitoring device is affixed to the user; and 2) processing circuitry coupled with the set of sensors, to detect that the user has been sedentary for a period of time, and cause the portable monitoring device to alert the user responsive to the detection to encourage the user to move.

A mobile terminal device as an exercise support device includes: an event information acquisition unit which acquires event information about an event for a user; an activity menu acquisition unit which acquires practice day information leading up to the event and an exercise menu; an exercise plan generation unit which generates an exercise plan using the event information, the practice day information, and the exercise menu; a pulse wave information acquisition unit which acquires pulse wave information of the user; and a physical condition determination unit which determines physical condition of the user based on the pulse wave information. The exercise plan generation unit modifies the exercise menu or the exercise plan, based on a result of the determination by the physical condition determination unit.

Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate metabolic equivalent of task (MET) measures for a plurality of moments of interest. Based on the MET measures and a time period for which the MET measures exceed a threshold value, it is determined whether the user is in a sedentary state. If the user is in the sedentary state, the user is provided a notification to encourage the user to perform a non-sedentary activity.