High-precision, energy-efficient electrodermal activity sensing system for stress detection

Abstract

Implementations of systems and methods for a high-accuracy, low-power electrodermal activity (EDA) signal acquisition system with an adaptive gain mechanism. In a first aspect, a device may receive an analog EDA sample amplified via an adaptive gain circuit comprising a first resistive ratio selected via a first multiplexer and a second multiplexer; determine that the sample is beyond a threshold value; select a second resistive ratio; and trigger the first multiplexer and second multiplexer to select the second resistive ratio. In another aspect, during a first iteration, a device may receive a plurality of EDA samples; determine whether the plurality of EDA samples are within the bounds of a first threshold and second threshold; and responsive to the determination, either maintain or adjust a number of the plurality of electrodermal activity samples received prior to the determination during a subsequent iteration.

Description

High-Precision, Energy-Efficient Electrodermal Activity Sensing System for Stress DetectionCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Application No. 63 / 574,570, filed April 4, 2024, the disclosure of which is herein incorporated by reference in its entirety for all purposes.TECHNICAL FIELD

[0002] The present embodiments generally relate to systems and methods for electrodermal activity sensing. In particular, the present embodiments relate to circuits and methods for high-precision gain control for electrodermal activity sensing.BACKGROUND

[0003] Electrodermal Activity (EDA) has emerged as a reliable stress indicator gaining traction in student, research, and commercial product development circles, especially with the rise of wearable devices. However, challenges remain in generalizing and deepening EDA data analysis. The tonic component of EDA data ranges from 0.01 pS to 40pS, and the phasic component from 0.01 pS to 5pS, necessitating systems capable of detecting a wide conductivity range and possessing high signal resolution.SUMMARY

[0004] In some aspects, the present disclosure is directed to implementations of systems and methods for a high- accuracy, low-power EDA signal acquisition system with an adaptive gain mechanism. One example implementation of the system was evaluated through simulation and a custom PCB, achieving below 1 % error and power consumption of 700pA under a 3.7V power supply.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings, wherein like reference numerals in the figures indicate like elements, and wherein:

[0006] FIG. 1 A is a graph illustrating an example of electrodermal activity (EDA) measured as conductivity over time, according to some implementations;

[0007] FIG. 1 B is an illustration of an electrical equivalent of human skin shown functionally and schematically, according to some implementations;

[0008] FIG. 2 is a block diagram of a conceptual model of a system for high-precision EDA capture and analysis, according to some implementations;

[0009] FIG. 3 is a block diagram of an implementation of a system for high-precision EDA capture and analysis;

[0010] FIG. 4A is a set of graphs comparing examples of skin resistance, output voltage, feedback ratio, and delta resistance per bit over skin resistance in a system for high-precision EDA capture and analysis, according to some implementations;

[0011] FIG. 4B is a graph of an example of filtered conductance over time, according to some implementations;

[0012] FIG. 5A is a set of graphs comparing examples of skin conductivity, sensor gain ratios, and output voltages, according to some implementations;

[0013] FIG. 5B is a set of graphs comparing resolution and output voltage for example circuits with and without implementations of adaptive gain;

[0014] FIG. 5C is a graph illustrating measurement accuracy, according to some implementations;

[0015] FIG. 5D is a set of graphs illustrating power consumption for example circuits with and without implementations of adaptive gain;

[0016] FIG. 6A is a flow chart of an implementation of a method for high-precision EDA capture and analysis;

[0017] FIG. 6B is a flow chart of an implementation of a method for high-precision EDA capture and analysis;

[0018] FIG. 7A is a photo of an implementation of a wearable device for high-precision EDA capture and analysis;

[0019] FIG. 7B is an illustration of another implementation of a wearable device for high-precision EDA capture and analysis;

[0020] FIG. 7C is a further illustration of the implementation of FIG 7B showing additional features;

[0021] FIG. 7D is a block diagram of a system for high-precision EDA capture and analysis, according to some implementations;

[0022] FIG. 7E is an illustration of another implementation of a wearable device for high-precision EDA capture and analysis; and

[0023] FIG. 8 illustrates a block diagram of a system within which aspects of the present embodiments may be implemented.DETAILED DESCRIPTION

[0024] In contemporary society, stress, as a prevalent mental health issue, impacts one in four individuals, with wide-ranging implications both mentally and physically. Psychological conditions like depression and anxiety, and in severe cases, suicide, are linked to stress. Physically, it is associated with serious health issues, including high blood pressure, strokes, and heart attacks. Furthermore, research suggests stress adversely affects the immune system, potentially increasing cancer risks. Stress also significantly impacts interpersonal relationships and workplace performance, leading to reduced quality of life. Currently, themost common way or the golden standard to determine the stress condition depends on the individuals' answers to the questionnaires, which are time-consuming and subjective. Thus, the development of an automatic stress detection system can replace the traditionally questionnaire-based method to minimize uncertainty and improve society's well-being.

[0025] Many studies have indicated that there is a strong correlation between the human body's physiological signals by control of The Hypothalamic-Pituitary-Adrenal (HPA) and the autonomic nervous system (ANS). Studies show that among all the physiological signals, Electrodermal Activity (EDA), or Galvanic Skin Response (GSR), is one of the most reliable stress indicators. As a result, EDA has emerged as a reliable stress indicator gaining traction in student, research, and commercial product development circles, especially with the rise of wearable devices.

[0026] However, challenges remain in generalizing and deepening EDA data analysis. EDA analysis requires measuring conductance of skin that is typically very low, usually on the order of Micro-Siemens (pS). When the body feel stress, it may trigger the ANS to release a rush of adrenaline, cortisol and other stress hormones for preparing a fight-or-flight response. Sweat glands may be activated by stress hormones, and higher temperatures may result in more secretion of sweat. The conductivity of skin depends upon many parameters, including the thickness of the stratum corneum which creates a potential barrier that prevents the current flows. Thus, the thicker the stratum corneum, the lower the conductance. Many other factors, including the individual, temperature, and most importantly, the active level of ANS caused by stress, could contribute to the variations of conductance. For example, FIG. 1A is a graph illustrating an example of EDA measured as conductivity over time, according to some implementations. In the example illustrated, sweat on the wrist can cause measurements to vary across a range of 0.1 pS to 20pS, more than two orders of magnitude.

[0027] FIG. 1 B is an illustration of an electrical equivalent of human skin shown functionally 150A and schematically 150B, according to some implementations. As shown at left in 150A, portions of skin including the stratum corneum and dermis may act as a series resistance that may be reduced due to sweat produced by a sweat. With additional pores, the skin can be represented as an equivalent circuit with a plurality of parallel resistors resulting in reduced resistance as more sweat is secreted, as shown at right in 150B,

[0028] The tonic component of EDA data ranges from 0.01 pS to 40pS, and the phasic component from 0.01 pS to 5pS, necessitating systems capable of detecting a wide conductivity range and possessing high signal resolution. Compromises in sensor electrode placement, due to user comfort and integration with devices like smartwatches, demand higher dynamic range and resolution. Some solutions involve high-resolution standalone analog-to-digital converters (ADCs), leading to increased cost and power consumption, resultingin bulkier devices or reduced battery life. Others fail to achieve high-resolution EDA signals, compromising prediction accuracy.

[0029] The present disclosure is directed to implementations of systems and methods for a high-accuracy, low- power EDA signal acquisition system with an adaptive gain mechanism. One example implementation of the system was evaluated through simulation and a custom PCB, achieving below 1 % error and power consumption of 700pA under a 3.7V power supply. Although primarily discussed in terms of EDA, similar implementations may use other biometric stress indicator signals, including heart rate variability (HRV) which may be detected by a wearable device at the wrist, for example; skin surface temperature; or other such stress signals.

[0030] As discussed above, the primary challenge in the measurement of Electrodermal Activity (EDA) or Galvanic Skin Response (GSR) signals lies in their extensive measurement range, typically spanning from 0.1 pS to 40 pS, equivalent to a resistor range of 10 MO to 25 KO. This wide range poses significant difficulties for EDA acquisition systems, particularly those that are low-cost and lack high-resolution Analog-to-Digital Converters (ADCs) and sensitivity. To address this, an EDA acquisition system may be implemented featuring an integrated adaptive gain selection mechanism. In some implementations, this system employs two MUXs to select appropriate resistors (R1 and R2) that are then connected to the OP circuits, enhancing the system’s dynamic range. The gain of the OP circuits is determined by the ratio of R1 to R2 (using the actual values of R1 , and R2 instead of labeled values), herein referred to as the Gain Ratio. This ratio is inversely proportional to the output voltage. The system may be ultra-low voltage (e.g. as low as 1 ,8V or lower), and may have high sensitivity using the adaptive feedback control system.

[0031] FIG. 2 is a block diagram of a conceptual model 200 of a system for high-precision EDA capture and analysis, according to some implementations. V+ is a 1 ,8V drive power applied on the surface of skin by electrodes 202. The material of electrodes 202 could be, but is not limited to, stainless steel, brass, silver, conductive fabric, or a gel-based material. In some implementations, the circuit may include two operation amplifiers (OP) 206A, 206B. First, OP 206A converts the current going through the skin to a voltage based on the conductance or resistance of the skin and feedback gain from the output. The feedback gain is driven by digital rheostat (DR) 208, discussed in more detail below, and may be controlled by a Microprocessor Controller Unit (MCU) 204. MCU 204 may control the value of DR according to the current EDA value. Second OP 206B may act as a voltage follower, increasing the input impendence and decreasing the output impendence. In some implementations, a bandpass filter 210 (either active or passive) may be used to filter out noise. The output may be converted into a digital signal or bitstream by analog-to-digital converter (ADC) 212.

[0032] FIG. 3 is a block diagram 300 of an implementation of a system for high-precision electrodermal activity capture and analysis, in some implementations. The block diagram 300 may operate at a sampling rate of 8Hz, in some implementations, meaning the interval between samples is 125 ms. During this interval, the microcontroller (MCU) 304 first retrieves the output voltage through an integrated analog-to-digital converter (ADC) 328. It then calculates the skin resistance based on the values of R1 and R2, subsequently transmitting this data to a smartphone or computer 308 via Bluetooth, where data may be displayed via a graphic user interface 310. Finally, the MCU 304 adjusts the values of R1 and R2 via multiplexers 324A, 324B and resistor sets 326A, 326B in response to the measured skin resistor 320, thereby preparing the system for the next data acquisition cycle.

[0033] In some implementations, MCU 304 may comprise an nRF52832 microcontroller unit MCU from Nordic, Inc., chosen for its multifunctionality, or any other suitable microcontroller. In some implementations, the MCU may include integrated Bluetooth Low Energy (BLE) capabilities and may comprise an ARM Cortex- M4 processor with a floating-point unit, operating at a frequency of 64 MHz, or any other suitable processor and clock rate

[0034] Power management system 306 may be based around a TP4054 chip (manufactured by Nan- Jing Top Power ASIC Corp) or any other suitable battery management circuit. The circuit may comprise an input charging mechanism such as a micro-USB port.

[0035] Power management system 306 may also comprise two DC-DC converter sub-circuits, which may comprise LM3671 ICs manufactured by Tl, Inc. or any suitable alternative. DC-DC converter sub-circuits may be employed as separate power sources for the MCU 304 and the Analog Front End (AFE) 302, both operating at 1.8V. This voltage may allow minimization of power consumption across all components, aligning with our goal of achieving the lowest possible power usage. DC power may be enabled and disabled via GPIO 280 under control of MCU 304 (e.g. disabling power to OP periodically to reduce power consumption).

[0036] Multiplexers (MUXs) 324A, 324B, referred to generally as multiplexers or MUXs 324, may comprise any suitable multiplexer, such as MAX4781 Multiplexers manufactured by Analog Devices, Inc. These multiplexers 324A, 324B may be used to select the resistors (e.g. by MCU 304) from resistor sets 326A, 326B that are connected to the operational amplifier (OP) circuit 322. OP may comprise any suitable type and form of amplifier, such as the TLV9061 manufactured by Tl, Inc.

[0037] To accurately evaluate the system's precision, both simulation-based resolution testing and on-board accuracy verification was performed. FIG. 5B is a set of graphs comparing resolution 510a and output voltage 510b for example circuits with and without implementations of adaptive gain. As used herein, resolution is defined in terms of ohms per bit, which quantifies the amount of resistor change that corresponds to a one-bit alteration in the ADC 328. Consequently, a lower value indicates better resolution, signifying the system's enhanced sensitivity to minor changes in the resistor. It is important to note that the Analog Front End (AFE) 302 is a non-linear system, meaning that resolution varies across different resistor ranges.

[0038] FIG. 4A is a set of graphs, according to some implementations of a system for high-precision EDA capture and analysis, comparing examples of:• skin resistance 400a (e.g. as captured between two electrodes);• output voltage 400b or the output voltage from the EDA acquisition system. In many implementations, the output range may be from 1 ,8V - 1 ,2V, which may be compatible with the embedded ADC. In the example illustrated, the voltage curves repeatedly return to the maximum because of changes in feedback ratio;• feedback ratio 400c, which may be changed by selection of different resistance ratios in a feedback path; and• delta resistance per bit over skin resistance 400d in a system a responsibility (Y-axis) is defined to evaluate the system's performance. The Y-axis is characterized by delta resistance (AR) over per ADC bit, which means how many resistance changes could lead to ADC output changes by 1 (either increase one or decrease 1). The lower value of the Y-axis, the more sensitive the system is. The red line in 400d is the performance of the system without the feedback system, and the black line is the performance of the system with the feedback system. As shown, the feedback system improves the performance significantly at resistances greater than 200K ohms, necessary for high-precision EDA capture and analysis, according to some implementations.

[0039] FIG. 4B is a graph 450 of an example of filtered conductance over time, according to some implementations. Unstable contact of the electrodes may result in noise. Due to the adaptive feedback system, if the output values are changing dramatically and frequently because of unstable skin contact, the feedback system will require adjustment frequently, which requires higher power consumption and processing steps, and increases the overall noise to the whole system. Thus, in some implementations as shown in FIG. 4B, a filter algorithm may be used to exclude or discard samples 454 beyond an upper and lower threshold 456A, 456B that are dynamically based on a sliding window of samples 452 (e.g. a moving average value, shown in solid line, plus and minus some amount such as 10% or 20%).

[0040] FIG. 5A is a set of graphs comparing examples of skin conductivity (500a), sensor gain ratios (500b), and output voltages (500c) over time, according to some implementations. For example, graph 500a depicts simulated EDA data ranging from 1-6 pS generated by using a Python library and characterized by an increasing trend and three distinct peaks (502, 504, 506). As skin conductivity fluctuates, particularly before the second peak 504, the output voltage correspondingly oscillates as shown in graph 500c. Upon reaching its saturation value (1 ,8V, determined by the power supply voltage), a compensation mechanism is activated by adjusting the Gain Ratio, as shown in graph 500b (changing from 1 .0 to 1 .8) This adjustment reduces the output voltage to prevent saturation.

[0041] An AFE configuration without an implementation of adaptive gain was simulated, mapping skin resistance ranging from 25kO to 10MO to an output voltage between 0 and 1 8V (the full range of the ADC). This is illustrated by the blue line in graph 510b. The resolution corresponding to each skin resistance value was calculated and is depicted by the blue line in graph 510a. The resolution begins at 34 Q / bit for a skin resistance of 25kQ and escalates to 311 kO / bit at 10MQ.

[0042] Simulated results for an AFE with an implementation of adaptive gain are shown as the orange line in graphs 510a and 510b. As shown, the adaptive gain implementations demonstrate significant improvements in resolution, particularly noticeable beyond the 300KQ skin resistance threshold.

[0043] This resolution starts at 31 Q / bit at 25kQ and reduces to a mere 4.6kQ / bit at 10MQ. A detailed comparison, particularly evident before 4MQ, is visible in the zoomed-in section 512 of graph 510a. Here, the orange line appears in discontinuous segments, each representing different gain settings. Graph 510b compares the output voltages of the AFE with (orange line) and without (blue line) adaptive gain. Notably, the output voltage from the AFE with adaptive gain is discontinuous, reflecting the implementation of various gains. Each continuous segment within this graph correlates to a specific gain setting, underscoring the dynamic adaptability of implementations of the system. Accordingly, implementations of the AFE and adaptive gain control systems and methods discussed herein demonstrate enhanced accuracy and resolution capabilities.

[0044] Measurement accuracy of implementations of the system were also tested on a physical prototype. A range of standard metal film resistors (specifically labeled 27k, 47k, 100k, 220k, 330k, 560k, 820k, 1 M, 2.2M, 3.3M, 5.1 M, and 10MQ) and measured as ranging from 27kQ to 10MQ were used to simulate skin resistance. These resistors were connected to the Analog Front End (AFE) 302 at sensor 320 as shown in FIG. 3 to simulate various levels of skin resistance.

[0045] Under control of the Microcontroller Unit (MCU), appropriate resistors R1 and R2 326A, 326B are selected via MUXs 324A, 324B based on the detected resistor level (in place of detected skin resistance, for this test). In some implementations, the MCU may calculate the resistor values (or the ratio) from the ADC reading. To determine the system's accuracy, the true resistor values were compared with the calculated values derived from the ADC. This comparison entailed analyzing the absolute difference between the calculated values and the true values, as shown in FIG. 5C.

[0046] From the data presented in the figure, it’s evident that the error values across all tested resistors fall below 1 %. It is important to note, however, that the absolute error values tend to increase as the skin resistance value increases. This trend is an expected characteristic in such measurements and underscores the importance of precision in higher resistor ranges. These results provide a comprehensive overview of the system's accuracy, demonstrating its reliability and effectiveness in various skin resistance scenarios

[0047] In order to meet the requirements for long-term monitoring in wearable devices, it may be important to optimize power consumption. This optimization may be approached from both hardware and firmware perspectives.

[0048] On the hardware front, two DC-DC converters may be utilized to reduce the battery voltage (ranging from 3.5-4.2V) down to 1.8V. In many implementations, DC-DC converters may be particularly efficient in converting voltage. In some implementations, both the Multiplexers (MUX) and operational amplifiers (OP) include an enable function; when disabled, they may consume little to no power. The MUX and OP may accordingly be activated only during the sampling period, and immediately disabled afterward, significantly conserving energy in some implementations.

[0049] The effectiveness of these power-saving measures is evident in the measurement results depicted in FIG. 5D. FIG. 5D is a set of graphs illustrating power consumption for example circuits with 540b and without 540a implementations of adaptive gain. The graph 540a provides a power analysis for implementations of the firmware that keeps the MUX and OP operational at all times, serving as a reference point. This analysis reveals two prominent current peaks under a 3.7V power supply, corresponding to Bluetooth broadcast events and data transmission from the board to a laptop. The baseline current in this scenario is about 1.97mA, with an average current of 2.127mA over a 10-minute power acquisition period. In contrast, 540b demonstrates a power analysis for an implementation of the firmware where the MUX and OP are activated only when necessary. A substantial reduction in the baseline current to 0.67mA may be observed, along with an average current of 721 pA over a similar 10-minute period. This dramatic drop in power consumption indicates that the sensing power consumption is the dominant factor compared to the MCU's power consumption.

[0050] Finally, a continuous data acquisition test was analyzed over 30 hours using a single 30mAh Li-ion battery in a lab environment. The results of this test demonstrated the system’s potential for long-term monitoring in highly compact wearable devices.

[0051] In some implementations, the tolerance of the gain selection resistors (R1 , R2) may be ±1 %. This tolerance can lead to inaccuracies in calculating skin resistance. For instance, a resistor labeled as 330KO (used as R1) was actually measured at 339KQ. To mitigate this, each resistor was precisely measured with an LCR meter, allowing the Microcontroller Unit (MCU) to calculate skin resistance using these actual values rather than the labeled ones, thereby enhancing accuracy. In some implementations, high- precision thin-film chip resistors, such as the P0402 series (±0.01%, VISHAY INTERTECHNOLOGY, INC), may be used to eliminate the need for individual resistor calibration.

[0052] For power consumption optimization, in some implementations, two DC-DC converters, with one constantly active to power the MCU and the other equipped with an enable function for the sensing system, may be utilized. The DC-DC converter may be periodically enabled and disabled in some implementations, insteadof the Multiplexers (MUX) and operational amplifiers (OP), which may further reduce power consumption. In an alternate implementation, a single DC-DC converter with disabled MUX and OP may be used to minimize power consumption. Some implementations may use a custom Bluetooth communication IC for further minimizing power consumption. The IC may use a customized low energy protocol that can consolidate approximately 15 seconds of data into a single package or packet, allowing for data transmission only every 15 seconds. Similar implementations may be used with other aggregation timings.

[0053] FIG. 6A is a flow chart of an implementation of a method 600 for high-precision EDA capture and analysis. At step 602, the electrodes are placed on the skin. At step 604, the system will set the initial feedback gain, and get the first ADC value at step 606. The MCU may calculate the EDA value based on the current gain (feedback ratio) and ADC value. After getting the EDA value, the MCU may check whether the current EDA value matches the current gain setting at 610. If not, the system will change the Gain accordingly at step 612. In some implementations, after a first iteration (e.g. once an average or baseline is identified), samples outside of a digital filter range may be removed at step 608.

[0054] FIG. 6B is a flow chart of an implementation of another method 650 for high-precision electrodermal activity capture and analysis. The operation of the embedded system's firmware centers around ADC sampling and the calculation of MUX selection values for determining the electrodermal activity (EDA) value within the system. This value directly impacts the system's feedback coefficient and may provide for highly accurate data representation and system responsiveness.

[0055] At step 652, the system starts with an initial MUX setting (e.g. selection of resistors), which may comprise a default or standard starting value This value may be preconfigured by a manufacturer or administrator, or may be based on a previous determined setting (e.g. during last use of the device, a baseline determined during a previous use, etc.).

[0056] At step 654, the system may measure skin resistance and determine an output value of the ADC. Upon completion of the ADC sampling process, at step 656, the system may compute a selection value for the MUXs, which affects the system's feedback coefficient, and set the MUX values at step 658. CThe determination of the EDA value is a function of both the ADC samples and the MUX selection values. Following this, the computed data is either transmitted via Bluetooth for immediate use or stored in flash memory for later retrieval at step 662.

[0057] Given that the computational process requires the activation of the microcontroller unit's (MCU) floatingpoint unit (FPU) 660, which significantly increases power consumption, in many implementations, a variable step computation method may be utilized to mitigate energy usage. Initially, with a sampling rate of 8Hz or any suitable frequency, the system computes the MUX selection values at a periodic interval, such as every 4 samples, equating to a computational frequency of 2Hz. A counter monitors the stability of the MUX selection values across these periods.

[0058] If the MUX selection values remain unchanged over a plurality of consecutive periods (e.g. 4 periods), the computation interval is adjusted (e.g. to every 16 periods (0 5Hz computational frequency)). Should the stability continue, the interval may be extended further (e.g. to every 32 periods, reducing the computational frequency to 0.25Hz). This adaptive strategy allows for a significant reduction in FPU activation time, conserving power without compromising the system's functional integrity or data accuracy.

[0059] Various numeric values are used in the present application, for example. The specific values are for example purposes and the aspects described are not limited to these specific values.

[0060] Accordingly, in many implementations, an Electrodermal Activity (EDA) acquisition system may be optimized for long-term wearability. A high-efficiency microcontroller may effectively integrate computational prowess with wireless communication capabilities in some implementations. Precise accuracy may be obtained over a wide range of skin resistance via a MUX-based resistor selection for adaptive gain control, essential for reliable monitoring.

[0061] In some implementations, strategic activation of key components may be applied during sampling to significantly reduce power consumption, a critical factor for wearable technology. This power-efficient design was validated through long-term endurance testing, underscoring the system’s suitability for extended use in compact wearable devices. Accordingly, the systems and methods discussed herein provide significant contributions to the field of biometric monitoring and wearable health technologies.

[0062] In various implementations, the system may be integrated into various forms such as wrist bands, smart clothing, smart rings, smart glasses, or other such devices. For example, FIG. 7A is a photo of an implementation of a wearable device for high-precision electrodermal activity capture and analysis, specifically a silicon band design, utilizing stainless steel for EDA / GSR electrodes. In some implementations, a Flexible Printed Circuit Board (FPCB) and an ultra-thin battery may be incorporated within the silicon band, aiming to create an unobtrusive device for long-term EDA monitoring.

[0063] FIG. 7B is an illustration of another implementation of a wearable device for high-precision EDA capture and analysis. Similar to the implementation of FIG. 7A, the device may be integrated with any other physiological sensors, such as heart rate sensors, which could provide a heart rate as well as heart rate variability; temperature sensors providing the surface temperature of skin; non-invasive blood glucose level testing; blood ketone levels; or any other such sensors.

[0064] FIG. 7C is a further illustration of the implementation of FIG. 7B showing an alternative placement of the electrodes. The electrodes may be integrated into the watch band to provide more stable contact between the skin and electrodes. Two wires are buried in the band to connect the electrodes and the main printed circuit board (PCB) in the body of the watch or wearable device.

[0065] FIG. 7D is a block diagram of a system for high-precision EDA capture and analysis, according to some implementations. A wearable device detects HR, HRV, temperature, and EDA data, or any other biometricor physiological data (e.g. blood glucose levels via non-invasive testing, blood ketone levels, etc.). The raw data may be transmitted to a smartphone or other portable computing device, where the data will be processed. In some implementations, extracted features may be fed to a Machine Learning model executed by the device. Thus, the detection of stress could be done in real-time. Finally, in some implementations, the data could be uploaded to one or more network servers (e.g. cloud storage or a cloud of virtual machines) for further processing or storage.

[0066] FIG. 7E is an illustration of another implementation of a wearable device for high-precision EDA capture and analysis. This system could be integrated with a steering wheel of a car. The EDA electrodes could be placed in anywhere in the steering wheel to monitor the EDA signal of the driver. As such, the emotional condition or fatigue level of the driver could be detected with the other physiological signals.

[0067] FIG. 8 illustrates a block diagram of an example of a system in which various aspects and embodiments can be implemented. System 800 may be embodied as a device including the various components described below and is configured to perform one or more of the aspects described in this application. Examples of such devices, include, but are not limited to, various electronic devices such as personal computers, laptop computers, smartphones, tablet computers, digital multimedia set top boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. Elements of system 800, singly or in combination, may be embodied in a single integrated circuit, multiple ICs, and / or discrete components. For example, in at least one embodiment, the processing and encoder / decoder elements of system 800 are distributed across multiple ICs and / or discrete components. In various embodiments, the system 800 is communicatively coupled to other systems, or to other electronic devices, via, for example, a communications bus or through dedicated input and / or output ports. In various embodiments, the system 800 is configured to implement one or more of the aspects described in this application.

[0068] The system 800 includes at least one processor 810 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this application. Processor 810 may include embedded memory, input output interface, and various other circuitries as known in the art. The system 800 includes at least one memory 820 (e.g., a volatile memory device, and / or a non-volatile memory device). System 800 includes a storage device 840, which may include non-volatile memory and / or volatile memory, including, but not limited to, EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk drive, and / or optical disk drive. The storage device 840 may include an internal storage device, an attached storage device, and / or a network accessible storage device, as non-limiting examples.

[0069] System 800 includes an encoder / decoder module 830 configured, for example, to process data to provide an encoded video / 3D object or decoded video / 3D object, and the encoder / decoder module 830 may include its own processor and memory. The encoder / decoder module 830 represents module(s) that may be included in a device to perform the encoding and / or decoding functions. As is known, a device may includeone or both of the encoding and decoding modules. Additionally, encoder / decoder module 830 may be implemented as a separate element of system 800 or may be incorporated within processor 810 as a combination of hardware and software as known to those skilled in the art.

[0070] Program code to be loaded onto processor 810 or encoder / decoder module 830 to perform the various aspects described in this application may be stored in storage device 840 and subsequently loaded onto memory 820 for execution by processor 810. In accordance with various embodiments, one or more of processor 810, memory 820, storage device 840, and encoder / decoder module 830 may store one or more of various items during the performance of the processes described in this application Such stored items may include, but are not limited to, the input video / 3D object, the decoded video / 3D object or portions of the decoded video / 3D object, the bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.

[0071] In several embodiments, memory inside of the processor 810 and / or the encoder / decoder module 830 is used to store instructions and to provide working memory for processing that is needed during encoding or decoding. In other embodiments, however, a memory external to the processing device (for example, the processing device may be either the processor 810 or the encoder / decoder module 830) is used for one or more of these functions. The external memory may be the memory 820 and / or the storage device 840, for example, a dynamic volatile memory and / or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory is used to store the operating system of a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM is used as working memory for coding and decoding operations.

[0072] The input to the elements of system 800 may be provided through various input devices as indicated in block 805. Such input devices include, but are not limited to, (i) an RF portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a composite input terminal, (ill) a USB input terminal, and / or (iv) an HDMI input terminal.

[0073] In various embodiments, the input devices of block 805 have associated respective input processing elements as known in the art. For example, the RF portion may be associated with elements suitable for (I) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) down converting the selected signal, (ill) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which may be referred to as a channel in certain embodiments, (iv) demodulating the down converted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, band-limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF portion may include a tuner that performs various of these functions, including,for example, down converting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband. Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and / or add other elements performing similar or different functions. Adding elements may include inserting elements in between existing elements, for example, inserting amplifiers and an analog-to-digital converter. In various embodiments, the RF portion includes an antenna.

[0074] Additionally, the USB and / or HDMI terminals may include respective interface processors for connecting system 800 to other electronic devices across USB and / or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, may be implemented, for example, within a separate input processing IC or within processor 810 as necessary. Similarly, aspects of USB or HDMI interface processing may be implemented within separate interface ICs or within processor 810 as necessary. The demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 810, and encoder / decoder module 830 operating in combination with the memory and storage elements to process the data stream as necessary for presentation on an output device.

[0075] Various elements of system 800 may be provided within an integrated housing, Within the integrated housing, the various elements may be interconnected and transmit data therebetween using suitable connection arrangement 815, for example, an internal bus as known in the art, including the I2C bus, wiring, and printed circuit boards.

[0076] The system 800 includes communication interface 850 that enables communication with other devices via communication channel 890. The communication interface 850 may include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 890. The communication interface 850 may include, but is not limited to, a modem or network card and the communication channel 890 may be implemented, for example, within a wired and / or a wireless medium.

[0077] Data is streamed to the system 800, in various embodiments, using a Wi-Fi network such as IEEE 802.11. The Wi-Fi signal of these embodiments is received over the communications channel 890 and the communications interface 850 which are adapted for Wi-Fi communications. The communications channel 890 of these embodiments is typically connected to an access point or router that provides access to outside networks including the Internet for allowing streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 800 using a set-top box that delivers the data over the HDMI connection of the input block 805. Still other embodiments provide streamed data to the system 800 using the RF connection of the input block 805.

[0078] The system 800 may provide an output signal to various output devices, including a display 865, speakers 875, and other peripheral devices 885. The other peripheral devices 885 include, in various examples ofembodiments, one or more of a stand-alone DVR, a disk player, a stereo system, a lighting system, and other devices that provide a function based on the output of the system 800. In various embodiments, control signals are communicated between the system 800 and the display 865, speakers 875, or other peripheral devices 885 using signaling such as AV. Link, CEC, or other communications protocols that enable device- to-device control with or without user intervention. The output devices may be communicatively coupled to system 800 via dedicated connections through respective interfaces 860, 870, and 880. Alternatively, the output devices may be connected to system 800 using the communications channel 890 via the communications interface 850. The display 865 and speakers 875 may be integrated in a single unit with the other components of system 800 in an electronic device, for example, a television. In various embodiments, the display interface 860 includes a display driver, for example, a timing controller (T Con) chip.

[0079] The display 865 and speaker 875 may alternatively be separate from one or more of the other components, for example, if the RF portion of input block 805 is part of a separate set-top box. In various embodiments in which the display 865 and speakers 875 are external components, the output signal may be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.

[0080] Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and / or use of specific steps and / or actions may be modified or combined. Additionally, terms such as "first”, "second”, etc. may be used in various embodiments to modify an element, component, step, operation, etc., for example, a “first transmission” and a “second transmission”. Use of such terms does not imply an ordering to the modified operations unless specifically required. So, in this example, the first transmission need not be performed before the second transmission, and may occur, for example, before, during, or in an overlapping time period with the second transmission.

[0081] The implementations and aspects described herein may be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed may also be implemented in other forms (for example, an apparatus or program). An apparatus may be implemented in, for example, appropriate hardware, software, and firmware. The methods may be implemented in, for example, an apparatus, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, for example, computers, cell phones, portable / personal digital assistants (“PDAs”), and other devices that facilitate communication of information between end-users.

[0082] Reference to “one embodiment” or “an embodiment” or “one implementation” or “an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout this application are not necessarily all referring to the same embodiment. Additionally, this application may refer to “determining” various pieces of information. Determining the information may include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.

[0083] Further, this application may refer to “accessing” various pieces of information. Accessing the information may include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0084] Additionally, this application may refer to “receiving” various pieces of information Receiving is, as with “accessing", intended to be a broad term. Receiving the information may include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, “receiving” is typically involved, in one way or another, during operations, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0085] It is to be appreciated that the use of any of the following “and / or", and “at least one of, for example, in the cases of “A / B”, “A and / or B" and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and / or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as is clear to one of ordinary skill in this and related arts, for as many items as are listed.

[0086] As will be evident to one of ordinary skill in the art, implementations may produce a variety of signals formatted to carry information that may be, for example, stored or transmitted. The information may include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal may be formatted to carry the bitstream of a described embodiment.Such a signal may be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting may include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries may be, for example, analog or digital information. The signal may be transmitted over a variety of different wired or wireless links, as is known. The signal may be stored on a processor-readable medium.

Claims

What is Claimed:

1. A method for adaptive gain controlled electrodermal activity sensing, comprising: receiving, by a device, an analog electrodermal activity sample amplified via an adaptive gain circuit comprising a first resistive ratio selected via a first multiplexer and a second multiplexer; determining, by the device, that the sample is beyond a threshold value; selecting, by the device, a second resistive ratio; and triggering, by the device, the first multiplexer and second multiplexer to select the second resistive ratio.

2. A method for adaptive gain controlled electrodermal activity sensing, comprising: receiving, by a device during a first iteration, a plurality of electrodermal activity samples; determining, by the device, whether the plurality of electrodermal activity samples are within the bounds of a first threshold and second threshold; and responsive to the determination, either maintaining or adjusting a number of the plurality of electrodermal activity samples received prior to the determination during a subsequent iteration; wherein, responsive to the plurality of electrodermal activity samples exceeding the bounds of the first threshold or second threshold, an adaptive gain ratio of an analog input circuit generating the electrodermal activity samples is adjusted.

3. A method for adaptive gain controlled electrodermal activity sensing, comprising: aggregating, by a device during a first time period, a number of electrodermal activity samples; determining, by the device, whether the aggregated electrodermal activity samples are within the bounds of a first threshold and second threshold; and either: responsive to a determination that the aggregated electrodermal activity samples are within the bounds of the first threshold the second threshold, increasing the number of electrodermal samples aggregated during a subsequent second time period, or responsive to a determination that the aggregated electrodermal activity samples are not within the bounds of the first threshold the second threshold, adjusting an adaptive gain ratio of an analog input circuit generating the electrodermal activity samples.

4. A wearable device configured to perform the method of any of claims 1-3.

5. One or more processors configured to perform the method of any of claims 1-3.

6. A biometric measurement device comprising a plurality of electrodes, and a circuit configured to perform the method of any of claims 1-3.

7. A portable computing device configured to perform the method of any of claims 1-3.

8. A first device comprising one or more processors configured to perform the method of any of claims 1 -3, and one or more transceivers configured to communicate with a second device.

9. An electronic circuit configured to perform the method of any of claims 1-3.

10. A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a device, cause the device to perform the method of any of claims 1-3.

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