ARRAYED ELECTRODES IN A WEARABLE DEVICE FOR DETERMINING PHYSIOLOGICAL CHARACTERISTICS
Embodiments relate generally to electrical and electronic hardware, computer software, wired and wireless network communications, and wearable computing devices in capturing and deriving physiological characteristic data. Techniques associated with an array of electrodes and methods are described, including selecting a subset of electrodes implemented on a wearable device, driving a first signal to a target location using the subset of electrodes, receiving a second signal from the target location, the second signal having a physiological component and a motion component, generating a raw physiological signal using a motion artifact reduction unit, generating a first physiological characteristic data using the raw physiological signal, and deriving a second physiological characteristic using the first physiological characteristic data.
1 . A method, comprising:
selecting a subset of a plurality of electrodes implemented on a wearable device;
driving a first signal to a target location using the subset of the plurality of electrodes;
receiving a second signal from the target location, the second signal comprising a physiological-related signal component and a motion-related signal component;
generating a raw physiological signal using a motion artifact reduction unit;
generating a bioimpedance data using the raw physiological signal; and
deriving a physiological characteristic using the bioimpedance data.
2 . The method of claim 1 , wherein the target location is adjacent to a source of a physiological characteristic.
3 . The method of claim 1 , wherein the selecting the subset of the plurality of electrodes comprises identifying two or more electrodes adjacent to the target location.
4 . The method of claim 1 , wherein the selecting the subset of the plurality of electrodes comprises testing two or more subsets of electrodes to identify the subset of the plurality of electrodes as optimal for capturing a raw sensor signal from the target location.
5 . The method of claim 1 , wherein the generating the raw physiological signal comprises subtracting a motion artifact signal from the second signal, the motion artifact signal being generated by a motion sensor.
6 . The method of claim 5 , wherein the motion artifact signal is associated with the motion-related signal component.
7 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises comparing a component of the raw physiological signal with the first signal.
8 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises amplifying at least a component of the raw physiological signal.
9 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises filtering at least a component of the raw physiological signal.
10 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises performing digital signal processing to generate heart rate data.
11 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises performing digital signal processing to generate respiration data.
12 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises deriving blood pressure data using one or more of the bioimpedance data, heart rate data and respiration data.
13 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises deriving maximal oxygen consumption (“VO2 max”) data using one or more of the bioimpedance data, heart rate data and respiration data.
14 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises comparing the bioimpedance data with an environmental factor.
15 . The method of claim 1 , wherein the deriving the physiological characteristic data comprises comparing the bioimpedance data with health and wellness information related to a wearer of the wearable device.
16 . The method of claim 1 , further comprising comparing one or both of the bioimpedance data and the physiological characteristic data with motion-related data being provided by a motion sensor.
17 . The method of claim 16 , wherein the motion-related data is associated with an activity level of a wearer of the wearable device.
18 . The method of claim 16 , wherein the motion-related data is associated with a type of activity being engaged in by a wearer of the wearable device.
19 . The method of claim 16 , wherein the motion-related data is associated with a stress level of a wearer of the wearable device.
20 . A method, comprising:
selecting a first subset and a second subset of a plurality of electrodes implemented on a wearable device, in a first mode, the first subset comprising two or more drive electrodes, the second subset comprising two or more sink electrodes;
capturing one or more data samples using the first subset and the second subset;
identifying an optimal subset of the plurality of electrodes using the one or more data samples, including determining the optimal subset adjacent to a target location;
capturing a sensor signal, in a second mode, using the optimal subset;
generating a raw physiological signal, including reducing a motion-related artifact in the sensor signal using a motion artifact reduction unit;
generating a bioimpedance data using the raw physiological signal; and
deriving a physiological characteristic using the bioimpedance data.