Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: Methods and systems are provided herein for monitoring a user's well-being. The methods may comprise collecting one or more sensor data, analyzing at least a subset of the collected sensor data, extracting features from the collected and/or analyzed sensor data, and determining one or more of a physical score, a psychological score and a total score. The methods may further comprise determining a user's well-being based on one or more of the scores.

Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: Examples are disclosed for determining a time-of-flight (ToF) of a wireless signal in a multipath wireless environment. In some examples a method for determining a time-of-flight (ToF) of a wireless signal in a multipath wireless environment may comprise receiving two or more wireless signals over a wireless communication channel from wireless device, determining a maximum likelihood solution for identifying a line-of-sight (LoS) signal of the two or more wireless signals, reducing the complexity of the maximum likelihood solution, determining a time that maximizes the reduced complexity maximum likelihood solution, and determining the ToF of the LoS signal based on the reduced complexity maximum likelihood solution and the determined time. Other examples are described and claimed.

Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

Abstract: A system for time-of-flight (ToF) positioning in an IEEE 802.11 network comprises an initiating station that transmits a request frame over a channel to a responding station for a ToF position measurement. The responding station may respond with an offloading of the channel information, request frame receipt time, and response frame transmit time back to the initiating station to enable the initiating station to calculate the ToF position with respect to the responding station.

Abstract: Embodiments of a communication station and method for transmission power control for Time-of-Flight (ToF) measurements in a wireless network are generally described herein. A protocol for fine timing measurements (FTMs) optimizes location performance rather than Wi-Fi coverage area and bit error rate by limiting an allowed maximum power and hence, EVM. A user equipment (UE) comprises a transceiver configured to receive, from an initiating station, a fine timing measurement request (FTMR) message at a maximum transmit power and lowest modulation and coding scheme (MCS), measure a relative received signal strength (RSSI) for the received FTMR message, determine a maximum transmit power, where the maximum transmit power is proportional to the measured RSSI; and transmit, to the initiating station, a fine timing measurement 1 (FTM1) message at the determined maximum transmit power and received lowest MCS.

Abstract: Embodiments for performing access point position determination using crowd sourcing are generally described herein. In some embodiments, a range report request is received, by at least one mobile device, from a network entity for determining a position of a plurality of access points (APs). The at least one mobile device performs range measurements on each of the plurality of APs at different locations. A range report associated with the range measurements performed on each of the plurality of APs is sent to the network entity by the at least one mobile device.

Abstract: Some demonstrative embodiments include devices, systems and/or methods of orientation estimation of a mobile device. For example, a mobile device may include an orientation estimator to detect a pattern in at least one image captured by the mobile device, and based on one or more geometric elements of the detected pattern, to determine one or more orientation parameters related to an orientation of the mobile device.

Abstract: Some demonstrative embodiments include devices, systems and/or methods of estimating a location of a mobile device. For example, an apparatus may include a wireless communication unit to communicate a message between an access point and a mobile device, the message including a group identifier to indicate the access point belongs to a group of two or more access points having local coordinates measured with respect to a common origin point.

Abstract: Some demonstrative embodiments include apparatuses, devices, systems and methods of range estimation. For example, a mobile device may include a radio to receive from an Access Point (AP) statistical channel information of a plurality of wireless communication channels in a region covered by the AP; a channel estimator to estimate, based on the statistical channel information, a time of arrival (ToA) of a wireless communication signal from the AP via a line of sight (LOS) channel between the AP and the mobile device; and a range estimator to estimate a range between the mobile device and the AP based on the ToA.

Abstract: Some demonstrative embodiments include devices, systems and/or methods of orientation estimation of a mobile device. For example, a mobile device may include an orientation estimator to detect a pattern in at least one image captured by the mobile device, and based on one or more geometric elements of the detected pattern, to determine one or more orientation parameters related to an orientation of the mobile device.

Abstract: Some demonstrative embodiments include devices, systems and/or methods of communicating filter information. For example, a device may include a wireless communication unit to perform the functionality of a first station (STA) to communicate with a second STA, the wireless communication unit is to communicate filter information including Transmit (Tx) filter information, the Tx filter information representing one or more Tx filter parameters of one or more Tx filters utilized by at least one STA selected from the group consisting of the first STA and the second STA.

Abstract: The disclosure generally relates to an enhanced positioning system and method using a combination or hybrid filter. In one embodiment, Time-Of-Flight (ToF) measurements are used to determine an approximate location for a mobile device in relationship to one or more Access Points. The ToF combined with known and unknown variables are then processed through a hybrid filter system to determine location of the mobile device. The hybrid filter system may include a Kalman Filter (KF) for processing linear models and generally Gaussian noise distribution. The KF assumes that the state probability of mobile device location is Gaussian. Such variables include, for example, WiFi ToF bias. The hybrid filter system may include a Bayesian Filter (BF) for processing variables having non-Gaussian noise distribution and non-linear models. Such variables include, for example, the coordinates of the mobile device. A probability determination from each of the KF and BF is then applied to estimate the mobile device location.

Abstract: Systems and methods are directed to use of a neighbor list for wireless indoor navigation. The neighbor list may include related information regarding all neighboring access points (APs). The neighbor list can be transmitted, at least partially, to include the related information of a desired number of or all APs in the neighbor list from one AP to a wireless device. The neighbor list can be transmitted in a Neighbor Report Response (NRR) or a time-of-flight (ToF) Response and allow the wireless device to scan for minimal number of APs for ToF measurements. By using the neighbor list, power consumption and time can be significantly reduced during wireless indoor navigation.