Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

Abstract: A method for detecting abnormal behavior involves constructing words and text documents based on data acquired from mobile phone sensors during defined time intervals. The time intervals are defined based on data from mobile phone sensors indicative of usage patterns of the mobile phone user. Words are constructed for each time interval as a vector including the time interval and sensor-based feature levels. Each sensor-based feature level is mapped to a range of values of a sensor-based feature that are extracted from the sensor data. The text document is constructed from the words based on the time intervals and the sensor-based feature levels. A current routine for each time interval is determined using topic modeling based on the words that most frequently appear in the text document. An alert is generated if the current routine for any time interval deviates from a past routine for a corresponding past time interval.

Abstract: A preventative healthcare system calibrates a risk model by assigning weights to attributes for the freshness, completeness and uncertainty of a user's medical information. A risk predictive model is implemented based on the medical information. The risk of a specific health outcome of the user is determined using the risk predictive model, which is calibrated by computing attribute scores for freshness, completeness and uncertainty of the medical information and by assigning weights to the attribute scores. A need-for-data (ND) score is computed using the weighted attribute scores. A need-for-checkup (NC) score is computed using traits of the user. The method determines that new medical information related to the user is needed or that the user needs a checkup based on the ND and NC scores. A prompt is delivered to the user indicating that new medical information related to the user is needed or that the user needs a checkup.

Abstract: A multi-antenna receiver within an uplink MIMO communication system is described. The antenna receiver includes an RRU and a BBU communicating between themselves through a fronthaul (FH) link. The multi-antenna receiver significantly decreases the fronthaul (FH) throughput requirements, while guaranteeing a low EVM between the time-domain signals transmitted from the users towards the RRU and the signals reconstructed and recovered at the BBU. The RRU processes the obtained frequency-domain signals in a space of dimensionality N by projecting them into a signal space of lower dimensionality (N?) and compressing them in the obtained projected signal subspace. At the BBU, the frequency-domain signals are decompressed and reconstructed on the projected signal subspace, before recovering the transmitted time-domain signals.

Abstract: The present disclosure relates to reception of radio frequency (RF) signals from one or more user communication devices at N RF signal receiving devices, wherein N is a positive integer, and to processing of the received RF signals by the one or more RF signal receiving devices. A RF signal receiving device decompresses compressed output signals received from one or more RF signal receiving devices of the N RF signal receiving devices and generates a compressed output signal by executing a signal compression with regard to the decompressed signals and with regard to a RF signal received by the RF signal receiving device. Additionally, the present disclosure is directed to a further processing of output signals of a RF signal receiving device of the N RF signal receiving devices at a signal processing device.

Abstract: A multi-antenna apparatus of a multi-input multi-output (MIMO) communication system for compressing and decompressing a frequency domain digital signal are provided, wherein a data channel type of each frequency-domain digital sub-signal of the frequency-domain digital signal is determined by a radio frame structure map (?).

Abstract: The present invention relates to a multi-antenna apparatus of a multi-input multi-output (MIMO) communication system for compressing and decompressing a frequency domain digital signal, wherein a data channel type of each frequency-domain digital sub-signal of the frequency-domain digital signal is determined by a radio frame structure map (?).

Abstract: A multi-antenna receiver within an uplink MIMO communication system is described. The antenna receiver includes an RRU and a BBU communicating between themselves through a fronthaul (FH) link. The multi-antenna receiver significantly decreases the fronthaul (FH) throughput requirements, while guaranteeing a low EVM between the time-domain signals transmitted from the users towards the RRU and the signals reconstructed and recovered at the BBU. The RRU processes the obtained frequency-domain signals in a space of dimensionality N by projecting them into a signal space of lower dimensionality (N?) and compressing them in the obtained projected signal subspace. At the BBU, the frequency-domain signals are decompressed and reconstructed on the projected signal subspace, before recovering the transmitted time-domain signals.

Abstract: The present disclosure relates to reception of radio frequency (RF) signals from one or more user communication devices at N RF signal receiving devices, wherein N is a positive integer, and to processing of the received RF signals by the one or more RF signal receiving devices. A RF signal receiving device decompresses compressed output signals received from one or more RF signal receiving devices of the N RF signal receiving devices and generates a compressed output signal by executing a signal compression with regard to the decompressed signals and with regard to a RF signal received by the RF signal receiving device. Additionally, the present disclosure is directed to a further processing of output signals of a RF signal receiving device of the N RF signal receiving devices at a signal processing device.