METHOD FOR OPTIMIZING A STATE OF A RIDER OF A VEHICLE BASED ON USING A WEARABLE SENSOR TO DETECT A CHANGE IN EMOTIONAL STATE OF THE RIDER
A method of optimizing a state of a rider of a vehicle. The method includes: receiving data from a wearable sensor worn by the rider of the vehicle indicative of an emotional state of the rider; comparing the data received from the wearable sensor to stored wearable sensor data in which quantitative patterns present in the stored sensor data are labelled as emotional states; determining a pattern of an emotional state based on the data received from the wearable sensor and the comparison to the stored wearable sensor data; detecting a change in emotional state of the rider based on a detected change in the data received from the wearable sensor indicative of a change in the emotional state of the rider; and adjusting an operational parameter of the vehicle in real time in response to the detected change in the emotional state of the rider.
1 . A method of optimizing a state of a rider of a vehicle, the method comprising:
receiving data from a wearable sensor worn by the rider of the vehicle indicative of an emotional state of the rider;
comparing the data received from the wearable sensor to stored wearable sensor data in which quantitative patterns present in the stored wearable sensor data are labelled as emotional states;
determining a pattern of the emotional state based on the data received from the wearable sensor and the comparison to the stored wearable sensor data;
detecting a change in the emotional state of the rider based on a detected change in the data received from the wearable sensor indicative of the change in the emotional state of the rider; and
adjusting an operational parameter of the vehicle in real time in response to the detected change in the emotional state of the rider.
2 . The method of claim 1 wherein detecting the change in the emotional state of the rider comprises:
receiving a first set of emotional state indicative wearable sensor data captured prior to the adjusting; and
receiving a second set of emotional state indicative wearable sensor data captured during the adjusting.
3 . The method of claim 1 wherein detecting the change in the emotional state of the rider comprises:
receiving a first set of emotional state indicative wearable sensor data captured prior to the adjusting; and
receiving a second set of emotional state indicative wearable sensor data captured after the adjusting.
4 . The method of claim 1 wherein the detecting the change in the emotional state includes detecting the change in the emotional state of the rider to one of: an unfavorable state, or a favorable state.
5 . The method of claim 1 wherein adjusting the operational parameter includes adjusting a powertrain of the vehicle.
6 . The method of claim 1 wherein adjusting the operational parameter includes adjusting a suspension system of the vehicle.
7 . The method of claim 1 wherein adjusting the operational parameter includes adjusting at least one of a route of the vehicle, an in-vehicle audio content, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a first proximity to objects along the route, and second proximity to other vehicles along the route.
8 . The method of claim 1 wherein receiving the data from the wearable sensor comprises receiving data from at least two of a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eyeglasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, or a necklace.
9 . The method of claim 1 wherein comparing the data received from the wearable sensor to the stored wearable sensor data includes classifying patterns of emotional state indicative wearable sensor data and associating the emotional state indicative wearable sensor data to emotional states and changes thereto from a training data set.
10 . The method of claim 9 wherein the training data set is sourced from at least one of: a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a ride helmet, a rider headgear, or a rider voice system.
11 . A data processing system configured to optimize a physiological state of a rider of a vehicle, the data processing system comprising:
a wearable sensor, worn by the rider, to communicate data indicative of a physiological condition of the rider;
a neural network to receive the communicated data and to:
compare the communicated data received from the wearable sensor to stored wearable sensor data in which quantitative patterns present in the stored wearable sensor data are labelled as physiological states;
determine patterns of the physiological state based on the data from the wearable sensor and the comparison to the stored wearable sensor data;
detect a change in the physiological state of the rider based on the determined patterns; and
adjust an operational parameter of the vehicle in real time in response to the detected change in the physiological state of the rider.
12 . The data processing system of claim 11 wherein the neural network detects a change in an emotional state of the rider by:
receiving a first set of emotional state indicative wearable sensor data captured prior to the adjusting; and
receiving a second set of emotional state indicative wearable sensor data captured during the adjusting.
13 . The data processing system of claim 11 wherein the neural network detects a change in an emotional state of the rider by:
receiving a first set of emotional state indicative wearable sensor data captured prior to the adjusting; and
receiving a second set of emotional state indicative wearable sensor data captured after the adjusting.
14 . The data processing system of claim 11 wherein the neural network detects a change in an emotional state by detecting the change in the emotional state of the rider to one of: an unfavorable state or a favorable state.
15 . The data processing system of claim 11 wherein the neural network adjusts the operational parameter by adjusting a powertrain of the vehicle.
16 . The data processing system of claim 11 wherein the neural network adjusts the operational parameter by adjusting a suspension system of the vehicle.
17 . The data processing system of claim 11 wherein the neural network adjusts the operational parameter by adjusting at least one of a route of the vehicle, an in-vehicle audio content, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a first proximity to objects along the route, or a second proximity to other vehicles along the route.
18 . The data processing system of claim 11 wherein the neural network receives the data from the wearable sensor by receiving data from at least two of: a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eyeglasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, or a necklace.
19 . The data processing system of claim 11 wherein the neural network compares the data received from the wearable sensor to the stored wearable sensor data by classifying patterns of emotional state indicative wearable sensor data and associating the emotional state indicative wearable sensor data to emotional states and changes thereto from a training data set.
20 . The data processing system of claim 19 wherein the training data set is sourced from at least one of: a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a ride helmet, a rider headgear, or a rider voice system.