Devices and methods for generating synthetic cardio-respiratory signals from one or more ballistocardiogram (BCG) sensors. A method for determining item specific parameters includes obtaining ballistocardiogram (BCG) data from one or more sensors, where the one or more sensors capture BCG data for one or more subjects in relation to a substrate. For each subject, the captured BCG data is pre-processed to obtain cardio-respiratory BCG data. The cardio-respiratory BCG data is sub-sampled to generate the cardio-respiratory BCG data at a cardio-respiratory sampling rate conducive to cardio-respiratory signal generation. The sub-sampled cardio-respiratory BCG data is cardio-respiratory processed to generate a cardio-respiratory parameter set. A synthetic cardio-respiratory signal is generated from at least the cardio-respiratory parameter set and a cardio-respiratory event morphology template. A condition of the subject is determined based on the synthetic cardio-respiratory signal.

Methods and systems for remote sleep monitoring are provided. Such methods and systems provide non-contact sleep monitoring via remote sensing or radar sensors. In this regard, when processing backscattered radar signals from a sleeping subject on a normal mattress, a breathing motion magnification effect is observed from mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing approaches for accurate heart-rate estimation to fail. Embodiments of the present disclosure use a novel active motion suppression technique to deal with this problem by intelligently selecting a slow-time series from multiple ranges and examining a corresponding phase difference. This approach facilitates improved sleep monitoring, where one or more subjects can be remotely monitored during an evaluation period (which corresponds to an expected sleep cycle).

An excreta identification device includes: a sound data acquisition unit that acquires sound data collected by a microphone arranged in a toilet; an excreta identification unit that identifies which of defecation, urination, and farting has been performed by inputting the acquired sound data to an identification model subjected to machine learning where sound data indicating any of defecation sound, urination sound, and farting sound is an input value, and which of defecation, urination, and farting has been performed is an output value; and an identification result output unit that outputs an identification result.

The present disclosure is directed to a method of collecting and using data obtained from an intelligent automated chair to optimize workflow and increase a user or group's health and productivity. Sensors in the intelligent automated chair can include motion, touch, heart rate, weight, presence, sound, keystroke, etc. In addition, posture, health, and productivity data are collected and compared over periods of time along with a learning algorithm to recommend specific operating models to increase a user or group's overall posture, health, and productivity. The methods and systems are configured to recommend action steps based on usage that can include a chair training program, nutritional training program, mental health training, bonus consideration, rewards program, advancement consideration, etc. Furthermore, the metrics and recommendations can assist with addressing issues associated with absenteeism and presenteeism. Lastly, data collected can be used to create forecasting models based on productivity and health cost.

There is described support structures for a bed, and various embodiments of a bed for use in a hospital. In an example, a support structure comprises a plurality of sections, each configured to support a respective part of a body, and a plurality of resilient members that extend in a longitudinal direction from an upper end of the support structure to a lower end of the support structure, wherein a shape and/or profile of the support structure is determined by a shape and/or profile of the resilient members.

Disclosed are a body composition measuring device using nine segments and an operation method thereof. The disclosed body composition measuring device comprises: both hand electrode parts and both foot electrode parts, each of which has a plurality of electrodes; two arm electrode parts attached to elbow joint regions of both arms, respectively; two leg electrode parts attached to knee joint regions of both legs, respectively; and a processing unit for causing electric current to flow through different combinations of the both hand electrode parts, the both foot electrode parts, the arm electrode parts, and the leg electrode parts at the time of measurement so as to measure impedance values of a measurement object, and then analyzing the body composition of the measurement object.

Described are systems for beds that can include sensors for sensing physical phenomena in an environment surrounding a bed, a display for outputting information about the environment, the bed, and a sleeper, and a controller communicably coupled to the sensors. The controller can receive the sensed physical phenomena from the sensors, analyze the physical phenomena to determine at least one of environmental, sleep, and health metrics of a sleeper in the bed, and determine, based on at least one of the environmental, sleep, and health metrics of the sleeper, control signals to modify the environment surrounding the bed. The controller can also output, at the display, the environmental, sleep, and health metrics of the sleeper. The controller can also transmit the control signals to a second controller in order to engage a home automation device. The physical phenomena can include ambient sound, ambient light, ambient CO2 concentration, and/or ambient temperature.

A monitoring system a user activity sensor to determine patterns of activity based upon the user activity occurring over time.

A posture measuring device, an intelligent cushion and an intelligent seat are disclosed. The sitting posture measuring device comprises two fiber optic sensors; a signal processing unit electrically connected to the two fiber optic sensors respectively; and a power supply unit electrically connected to the signal processing unit. The sitting posture measuring device further comprises a prompting unit and/or a wireless communications unit electrically connected to the signal processing unit. The two fiber optic sensors are configured from left to right or from front to back. The fiber optic sensors detect changes in optical signals generated from changes in surface pressure on the sensors, and the signal processing unit analyzes a posture of a user on the basis of changes in optical signals generated from changes in surface pressure on the sensors.

A smart mirror can show live or recorded streaming video of an instructor performing a workout in a package that is attractive and unobtrusive enough to hang in a living room. The smart mirror includes a mirror surface with a fully reflecting section and a partially reflecting section. A display behind the partially reflecting section shows the video when the smart mirror is on and is almost invisible when the smart mirror is off. The smart mirror also has a speaker, a microphone, and a camera to enable a user to view the video content and interact with the instructor. The smart mirror may connect to the user's smart phone, a peripheral device (e.g., a Bluetooth speaker) to augment user experience, a biometric sensor to provide biometric data to assess user performance, and/or a network router to connect the smart mirror to a content provider, an instructor, and/or other users.