A system and method for adaptive speech and biofeedback control of sexual stimulation devices. In an embodiment, the system and method involve receiving audio from a microphone, processing the audio through an automated speech detection engine to detect speech within the audio, matching the speech to a control command for a sexual stimulation device, generating a control signal for the sexual stimulation device based on the control command, receiving biometric data from a biometric sensor, and adjusting the control signals based on the biometric data before outputting the adjusted control signal for use in operating the sexual stimulation device. In some embodiments, the adjustment to the control signal is made automatically by a machine learning algorithm using the command and biometric data as inputs.

A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.

A system and method for automated generation of control signals for sexual stimulation devices from videos of sexual activity. The system and method use annotation data indicating comprising indications of movements in the video corresponding to sexual activity to generate control signals for compatible sexual stimulation devices. The annotation data may be generated manually or automatically. Device control signals may be generated directly from the annotation data and synchronized with a video, or may be processed through a series of machine learning algorithms to generate models of “typical” sexual activity represented in the videos, which models are then used to generate signals for the sexual stimulation device.

A percussive therapy system that includes a percussive massage device including a network interface, and an intelligence engine. The intelligence engine is configured to receive manual capture data and real-time tracking data from the percussive massage device, receive remote data from a remote data source, and generate recommendation data comprising a recommended protocol to be performed by the percussive massage device. The recommendation data is generated from demographic, activity, temporal, analytics, and biometric data, received from the manual capture data, the real-time tracking data, and the remote data inputs.

A mat assembly that includes a mat that includes a top layer and a bottom layer, a plurality of vibration motors positioned between the top layer and the bottom layer, a plurality of sensors associated with the mat, a removable battery pack, and a controller. In use, a first set of vibration motors within the plurality of vibration motors is configured to be actuated when a first one of the plurality of sensors is actuated.

A system and method for automated generation of control signals for sexual stimulation devices from usage history and other data. The system and method involve analyzing historical usage and other data for a user for a device or devices, processing the data through machine learning algorithms, and generating new or recombined patterns of stimulation based on the outputs from the machine learning algorithms. The resulting automated control signals represent partially or fully customized stimulation for a given user which evolve over time as the user continues to use the device or devices.

A system and method for automated generation of control signals for sexual stimulation devices from usage history and other data. The system and method involve analyzing historical usage and other data for a user for a device or devices, processing the data through machine learning algorithms, and generating new or recombined patterns of stimulation based on the outputs from the machine learning algorithms. The resulting automated control signals represent partially or fully customized stimulation for a given user which evolve over time as the user continues to use the device or devices.

A garment assembly includes a sleeve member having outer and inner surfaces, a first vibration assembly associated with the sleeve member, and a control module associated with the sleeve member. The first vibration assembly includes a plurality of vibration motors that are arranged in a circle about a center point. An angular distance between each vibration motor of the plurality of vibration motors is approximately the same. The control module includes a battery, and the first vibration assembly is in electrical communication with the control module.

A garment assembly that includes a garment member with an inner surface and an outer surface, a power source, and at least a first linear motor positioned adjacent the inner surface of the garment member. The first linear motor is electrically coupled to the power source and includes a shaft member that includes a magnet. A distal end of the shaft member is configured to reciprocate against the inner surface of the garment member.

A system and method for automated generation of control signals for sexual stimulation devices from videos of sexual activity. The system and method use annotation data indicating comprising indications of movements in the video corresponding to sexual activity to generate control signals for compatible sexual stimulation devices. The annotation data may be generated manually or automatically. Device control signals may be generated directly from the annotation data and synchronized with a video, or may be processed through a series of machine learning algorithms to generate models of “typical” sexual activity represented in the videos, which models are then used to generate signals for the sexual stimulation device.