Spectroscopic device

Abstract

The disclosure is in the field of spectroscopic devices. The disclosure relates to a spectroscopic device for determining a concentration of a body substance of a person comprising: a. a spectroscopy module for acquiring spectroscopic data measured at two body locations of the person, wherein the spectroscopic data comprises a first spectrum of a first body location and a second spectrum of a second body location, b. a temperature sensor for acquiring temperature data measured at the two body locations of the person, wherein the temperature data comprises a first temperature from the first body location and a second temperature of the second body location, c. a processor for determining the concentration of the body substance of the person using the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data, and d. an output for outputting the concentration of the body substance of the person.

Description

[0001] Spectroscopic Device

[0002] The disclosure is in the field of spectroscopic devices. The disclosure relates to a spectroscopic device for determining a concentration of a body substance of a person, a vehicle comprising the spectroscopic device, a method for determining a concentration of a body substance of a person, a use of the concentration of the body substance of the person for controlling a functionality of a vehicle, and a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method for determining a concentration of a body substance of a person.

[0003] Background

[0004] The measurement of body substances of a person plays a role in many different areas. Examples are the medical sector, for example diabetes patients need to regularly measure their blood glucose level. Another example is the vehicle security sector in which intoxicants like alcohol or drug blood concentrations of the driver need to be measured. Spectroscopy is an attractive method to determine body substances as it is non-invasive and hence enjoys high acceptance of users.

[0005] CN104827900 A and KR10-2022-0083289 A disclose an infrared detector built into the handle of the transmission of a car. The detector measures the alcohol content of the hand when touching the handle. However, differences in the spectra due to the presence of blood alcohol are so small that reliable measurement results can hardly be obtained by only evaluating one spectrum.

[0006] US 2022 / 160309 A1 discloses a plurality of interconnected devices for acquiring and analyzing physiological data from a user. However, the sensitivity for compounds of low concentrations is limited.

[0007] US 2023 / 204507 A1 discloses system for non-invasively measuring an analyte in a vehicle driver and controlling a vehicle based on a measurement of the analyte. However, the sensitivity for compounds of low concentrations is limited.

[0008] US 2024 / 008812 A1 discloses a wrist wearable device with non-invasive sensing for the detection of alcohol, drug use or abuse. However, sensitivity and reliability may still require improvement.

[0009] It was hence the object of the present disclosure to provide a reliable and comfortable solution to determining a concentration of a body substance of a person.

[0010] Summary In one aspect the disclosure relates to spectroscopic device for determining a concentration of a body substance of a person comprising: a) a spectroscopy module for acquiring spectroscopic data measured at two body locations of the person, b) a temperature sensor for acquiring temperature data measured at the two body locations of the person, c) a processor for determining the concentration of the body substance of the person using the spectroscopic data and the temperature data, and d) an output for outputting the concentration of the body substance of the person.

[0011] In another aspect the disclosure relates to spectroscopic device for determining a concentration of a body substance of a person comprising: a) a spectroscopy module for acquiring spectroscopic data measured at two body locations of the person, wherein the spectroscopic data comprises a first spectrum of a first body location and a second spectrum of a second body location, b) a temperature sensor for acquiring temperature data measured at the two body locations of the person, wherein the temperature data comprises a first temperature from the first body location and a second temperature of the second body location, c) a processor for determining the concentration of the body substance of the person using the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data, and d) an output for outputting the concentration of the body substance of the person.

[0012] In another aspect the disclosure relates to a vehicle comprising the spectroscopic device according to the disclosure.

[0013] In another aspect the disclosure relates to a method for determining a concentration of a body substance of a person comprising: a) receiving spectroscopic data of the person measured at two body locations of the person, b) receiving temperature data measured at the two body locations of the person, c) determining the concentration of the body substance of the person using the spectroscopic data and the temperature data, and d) outputting the concentration of the body substance of the person.

[0014] In another aspect the disclosure relates to a method for determining a concentration of a body substance of a person comprising: a) receiving spectroscopic data of the person measured at two body locations of the person, wherein the spectroscopic data comprises a first spectrum of a first body location and a second spectrum of a second body location, b) receiving temperature data measured at the two body locations of the person, wherein the temperature data comprises a first temperature from the first body location and a second temperature of the second body location, c) determining the concentration of the body substance of the person using the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data, and d) outputting the concentration of the body substance of the person.

[0015] In another aspect the disclosure relates to a use of the concentration of the body substance of the person obtained from the method of any of the previous claims for determining the person's fitness to drive a vehicle.

[0016] In another aspect the disclosure relates to a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: a) receiving spectroscopic data of the person measured at two body locations of the person, b) receiving temperature data measured at the two body locations of the person, c) determining the concentration of the body substance of the person using the spectroscopic data and the temperature data, and d) outputting the concentration of the body substance of the person.

[0017] In another aspect the disclosure relates to a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: a) receiving spectroscopic data of the person measured at two body locations of the person, wherein the spectroscopic data comprises a first spectrum of a first body location and a second spectrum of a second body location, b) receiving temperature data measured at the two body locations of the person, wherein the temperature data comprises a first temperature from the first body location and a second temperature of the second body location, c) determining the concentration of the body substance of the person using the the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data, and d) outputting the concentration of the body substance of the person.

[0018] In another aspect the disclosure relates to spectroscopic device for determining a concentration of a body substance of a person comprising: a) a first spectroscopy module for acquiring spectroscopic data comprising a first spectrum measured at a body location of the person, b) a second spectroscopy module for acquiring spectroscopic data comprising a second spectrum measured at a second body location of the person, c) a processor for determining the concentration of the body substance of the person using the first spectrum and the second spectrum, and d) an output for outputting the concentration of the body substance of the person.

[0019] In another aspect the disclosure relates to a method for determining a concentration of a body substance of a person comprising: a) receiving spectroscopic data comprising a first spectrum measured at a first body location of the person and comprising a second spectrum measured at a second body location of the person, b) determining the concentration of the body substance of the person using the first spectrum and the second spectrum, and c) outputting the concentration of the body substance of the person.

[0020] In another aspect the disclosure relates to a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: a) receiving spectroscopic data comprising a first spectrum measured at a first body location of the person and comprising a second spectrum measured at a second body location of the person, b) determining the concentration of the body substance of the person using the first spectrum and the second spectrum, and c) outputting the concentration of the body substance of the person.

[0021] The advantage of the present disclosure is that the concentration of a body substance of a person can be determined more accurately within short measurement time. This is because the combination of two different body parts together with the temperature allows a more accurate determination of the background, in particular fluctuations due to thermal and / or local effects as well as noise. The method is hence robust against changes of the background, i.e. signals which are not caused by the body substance of interest. Further, the method allows the detection of inaccuracies of the measurement, for example improper placement of a body part with regard to the spectrometer. Hence, the method enables the reliable detection of body substances of low concentration which are otherwise not measurable as their signals are hidden behind other signals.

[0022] The spectroscopic device may be integrated into a vehicle including cars, motorcycles, buses, trucks, trams, trains or even airplanes, hence a vehicle may comprise a spectroscopic device. The spectroscopic device may be suitable for integration into a vehicle. The spectroscopic device may be attached to the vehicle, or it may be integrated as component or as part of a component of a vehicle, for example as part of a display in the dashboard, an entertainment control system, or loudspeakers. The spectroscopic device can be placed at various places, for example in the steering wheel and its periphery, such as the steering wheel rim, the steering wheel column, or the steering wheel center behind or besides the emblem; in the dashboard, such as in the instrument cluster bezel or its surrounding, the dashboard button panel or in or around frequently used buttons like the infotainment control button or engine start button, the touchscreen display in the center display of the infotainment system; the overhead and A-pillars, such as in the overhead console behind the light sensor, nestled behind the light sensor housing in the overhead console, the A- pillar trim on the driver side placed behind the A-pillar trim panel; the center console, such as the cup holder insert incorporated within a removable cup holder insert, the gear shift knob positioned on top or on the side of the gear shift knob, the arm rest, the parking break button.

[0023] The spectroscopic device may be integrated into consumer electronics products, in particular in consumer electronic products which are worn on the body, for example a smartwatch, head phones, hearing aids, continuous glucose monitoring (CGM) systems, wearable medical devices, such as blood pressure, temperature, and oxygen levels, virtual reality headsets, smart glasses, skin patches, sleep trackers. The spectroscopic device may be integrated into apparel, for example in a hat, a helmet, a shirt, a scarf, a belt, underwear, shoes, or socks.

[0024] The term "spectroscopic device” may refer to an apparatus which is capable of recording spectroscopic data of a person. The spectroscopic device may be a spectrometer or a device into which a spectrometer is integrated. The spectroscopic device may be portable or stationary, for example a laboratory device. A portable spectroscopic device may be a hand-held spectrometer or a module which is integrated into a portable device like a smartphone, a tablet or a wearable like a smartwatch. A portable spectroscopic device may be communicatively coupled to a computer device, for example a cloud computer or a smartphone. Such computer device may be configured to execute a chemometric model. The computer device may further be configured to receive spectroscopic data from the spectroscopic device. The computer device may store such spectroscopic data, or send it to a system for determining concentration of a body substance.

[0025] A spectroscopic device may comprise a spectroscopy module comprising:

[0026] - an optical element configured for separating incident optical radiation provided by the measurement into a spectrum of constituent wavelength components;

[0027] - a photosensor comprising at least one photosensitive region configured for receiving the optical radiation from the optical element, wherein the photosensor is configured for generating at least one photosensor signal dependent on an illumination of the photosensitive region by the optical radiation.

[0028] The term "optical element” may refer to an arbitrary element configured for influencing optical radiation. The optical element may be configured for at least one of at least partially dispersing the optical radiation, at least partially filtering the optical radiation, at least partially reflecting the optical radiation, e.g. diffusely or directly, at least partially deflecting the optical radiation, at least partially transmitting the optical radiation and at least partially absorbing the optical radiation. The optical element may comprise at least one of a prism, a grating, a beam splitter, or an interferometer, for example a Michelson interferometer. The optical element may be configured for being used in mobile applications, for example for being used in handheld spectrometer devices and / or in spectrometer devices comprised by electronic communication devices, such as a smartphone or a tablet. As another example, the optical element may comprise at least one optical filter element. The optical filter element may be configured for filtering the optical radiation or more specifically at least one selected spectral range of the optical radiation. The optical filter element may specifically be positioned in a light path before the photosensor. As an example, the portable spectrometer may comprise a plurality of a photosensors, for example 5 to 20, such as 8 to 12. The photosensors may be arranged as pixels in an array or in a matrix. The portable spectrometer may comprise a plurality of optical filter elements. An optical filter element may be positioned in a beam path before a photosensor. The optical filter elements may be transmissive at different wavelengths of different wavelength regions. For example, each photosensor may be positioned behind an optical filter with regard to the beam path, wherein each optical filter is transmissive at different wavelength or different wavelength region to the other optical filters.

[0029] The spectroscopy module may comprise one or more than one photosensor. The photosensor may comprise at least one photosensitive region. The photosensitive region may be configured for receiving the optical radiation from the optical element. The photosensor may be configured for generating at least one photosensor signal dependent on an illumination of the photosensitive region by the optical radiation. The term "sensor” may refer to a device configured for detecting at least one condition or for measuring at least one measurement variable. The sensor may be capable of generating at least one signal, such as a measurement signal, which is a qualitative or quantitative indication of the measurement variable and / or measurement property, e.g. of an illumination of the sensor or a part of the sensor. The signal may be or comprise an electrical signal, such as a current, specifically a photocurrent. The term "photosensor” may refer to a sensor or a detector configured for detecting or measuring optical radiation, such as for detecting an illumination and / or a light spot generated by at least one light beam, e.g. by using the photoelectric effect. The photodetector may comprise at least one substrate. As an example, a single photosensor may be a substrate with at least one single photosensitive region, which generates a physical response, e.g. an electronic response, to the illumination for a given wavelength range.

[0030] The term "photosensitive region” may refer to a unit of the photosensor, specifically to a spatial area or volume being part of the photosensor, configured for being illuminated, or in other words for receiving optical radiation, and for generating at least one signal, such as an electronic signal, in response to the illumination. The photosensitive region may be located on a surface of the photosensor. The photosensitive region may specifically be a single, closed, uniform photosensitive region. However, other options may also be feasible.

[0031] The spectroscopic device may comprise at least one light emitting element configured for emitting illumination light for illuminating the person in order to generate detection light from the person. The light emitting element may be an incandescent lamp, for example a tungsten filament lamp or a tungsten halogen lamp, a light-emitting diode (LED), a laser diode, a gas-discharge lamp, for example a xenon lamp, a mercury vapor lamp, or a deuterium lamp.

[0032] The term "light” may refer to electromagnetic radiation in one or more of the infrared, the visible and the ultraviolet spectral range. The term "ultraviolet spectral range” may refer to electromagnetic radiation having a wavelength of 1 nm to 380 nm, preferably of 100 nm to 380 nm, for example 280 nm to 315 nm (UV-B) or 315 nm to 380 nm (UV-A). Further, in partial accordance with standard ISO-21348 in a valid version at the date of this document, the term "visible spectral range” may refer to a spectral range of 380 nm to 760 nm. The term "infrared spectral range” (IR) may refer to electromagnetic radiation of 760 nm to 1000 m, wherein the range of 760 nm to 1.5 pm is usually denominated as "near infrared spectral range” (NIR) while the range from 1.5 p to 15 pm is denoted as "mid infrared spectral range” (MidlR) and the range from 15 pm to 1000 pm as "far infrared spectral range” (FIR). Preferably, light used for the typical purposes of the present disclosure is light in the infrared (IR) spectral range, more preferred, in the near infrared (NIR) and / or the mid infrared spectral range (MidlR), especially the light having a wavelength of 750 nm to 2.5 pm, for example 780 nm to 1 .4 pm or 1 .4 pm to 2.5 pm. These wavelength regions are particularly suitable for obtaining material properties of a person.

[0033] The spectroscopic device may comprise a processor to process the photosensor signals into spectroscopic data, for example an infrared spectrum. The processor may output the spectroscopic data, for example to an interface for further processing or to a user interface. The processor may further be configured to apply a chemometric model and output the concentration of a body substance obtained by the chemometric model. The spectroscopic device may further comprise a memory. The memory may be configured to store the chemometric model. The memory may be configured to store spectroscopic data.

[0034] The spectroscopic device may contain or be placed behind a transparent display. The term "display” may refer to an arbitrary shaped device configured for displaying an item of information. The item of information may be arbitrary information such as at least one image, at least one diagram, at least one histogram, at least one graphic, text, numbers, at least one sign, or an operating menu. The display may be or may comprise at least one screen. The display may have an arbitrary shape, e.g. a rectangular shape. The display may be a front display of a device.

[0035] The display may be or may comprise at least one organic light-emitting diode (OLED) display. The term "organic light emitting diode” may refer to a light-emitting diode (LED) in which an emissive electroluminescent layer is a film of organic compound configured for emitting light in response to an electric current. The OLED display may be configured for emitting visible light. The display, particularly a display area, may be covered by glass. In particular, the display may comprise at least one glass cover.

[0036] The transparent display may be at least partially transparent. The term "at least partially transparent” may refer to a property of the display to allow light, in particular of a certain wavelength range, e.g. in the infrared spectral region, in particular in the near infrared spectral region, to pass at least partially through. For example, the display may be semitransparent in the near infrared region. For example, the display may have a transparency of 20 % to 50 % in the near infrared region. The display may have a different transparency for other wavelength ranges. For example, the display may have a transparency of > 80 % for the visible spectral range, preferably > 90 % for the visible spectral range. The transparent display may be at least partially transparent over the entire display area or only parts thereof. Typically, it is sufficient if only those parts of the display area are at least partially transparent trough which light needs to pass from the projector or to the camera.

[0037] The display may comprise a display area. The term "display area” may refer to an active area of the display, in particular an area which is activatable. The display may have additional areas such as recesses or cutouts. The display may have a first area associated with a first pixel per inch (PPI) value and a second area associated with a second PPI value. The first PPI value may be lower than the second PPI value, preferably first PPI value is equal to or below 400 PPI, more preferably the second PPI value may be equal to or higher than 300 PPI. The first PPI value may be associated with the at least one continuous area being at least partially transparent.

[0038] The spectroscopy module may be positioned such that it can illuminate the person with light through the transparent display. The spectroscopy module may be positioned such that it can receive light from the person through the transparent display. Light reflected or refracted from the person firstly crosses the transparent display before it impinges on the sensor of the spectroscopy module. From the person's view, the spectroscopy module may be placed behind the transparent display.

[0039] The term "body substance” may refer to any chemical substance which can be found in a human body, in particular in the skin, blood or interstitial fluid of a human body. The body substance may be indicative of the person's fitness to drive a vehicle, the body substance may, for example, reduce the concentration of a person or may be a metabolite of such substance. The body substance may be indicative for a health or fitness condition which compromises the person's fitness, for example a low hydration level or an irregular blood glucose concentration. Body substance may comprise proteins, such as enzymes, antibodies, or hormones; carbohydrates, such as glucose, glycogen, or fructose; lipids, such as triglycerides, cholesterol, and phospholipids; water; nucleic acids, such as DNA or RNA; amino acids, such as alanine, glutamic acid, cysteine; neurotransmitters, such as dopamine, serotonin, and acetylcholine; hormones, such as insulin, estrogen, or testosterone; electrolytes, such as sodium, potassium, or calcium ions; vitamins, such as ascorbic acid, calciferol, cobalamin; metabolites, such as lactate, urea, and creatinine.

[0040] Body substance may be an intoxicant or its metabolite including ethanol, opioids, such as heroin, morphine, fentanyl; stimulants, such as amphetamine, methylphenidate, cocaine; benzodiazepines, such as diazepam, or alprazolam; cannabinoids, such as tetrahydrocannabinol (THC); barbiturates, such as phenobarbital; hallucinogens, such as lysergic acid diethylamide (LSD) or psilocybin; antihistamines, such as diphenhydramine; antipsychotics and antidepressants, such as fluoxetine or amitriptyline; muscle relaxants, such as carisoprodol or cyclobenzaprine; pain killers, such as tramadol, codeine, ibuprofen, naproxen, cyclobenzaprine, or methocarbamol.

[0041] The term "spectroscopic data” may refer to data associated with a spectroscopic measurement of a person, in particular with optical spectroscopic measurement of the person. The spectroscopic data may be received from the spectroscopic device of the present disclosure. The spectroscopic data may be received directly from a spectroscopic device or indirectly, i.e. from a storage device to which the spectroscopic data have been stored after the measurement. A spectroscopic measurement may be triggered by a predefined event, for example when the vehicle is switched on, before the engine is started, or after a certain time period. A spectroscopic measurement may be triggered when a measurement trigger event occurs. A measurement trigger event may be a situation in which an indicator indicates the necessity for a spectroscopic measurement necessary. A measurement trigger event may occur when an indicator indicate that the person's fitness to drive is potentially compromised, for example due to intoxicants such as alcohol or drugs, due to a health problem, for example low sugar concentration of a diabetes patient, or due to fitness problems like dehydration. The measurement trigger event may be determined using person data and / or environmental data. For example, the person data and / or environmental data may indicate an increased likelihood that the person's fitness to drive the vehicle are compromised, such as slow pupil reflex recorded by an optical camera, unusual movement patterns recorded by a pressure sensor, or certain voice characteristics recorded by a microphone. Triggering a spectroscopic measurement in such cases may be particularly useful if the body substance is used for access control of the vehicle, for example to keep drunk persons from driving without burdening obviously sober persons with a measurement.

[0042] The spectroscopic measurement may be made at various body parts of the person, for example the face, the arm, the hand. The spectroscopic measurement may be made at parts of the hand, for example the palm, the back of the hand, one or multiple fingers, such as the thumb, the forefinger, the long finger, the ring finger or auricular finger. The spectroscopic measurement may be made in direct contact with the person or in close proximity, for example with a distance of less than 10 cm or less than 5 cm between person and spectrometer device. The spectroscopic measurement is made at two body locations of the person. The two body locations may be apart from each other by at least 1 cm, preferably at least 5 cm, such as at least 10 cm, in particular at least 20 cm. An examples of different body locations is the right and the left hand of the person, for example if the spectroscopic device is integrated into an object which is touched by both hands like a steering wheel. Another example is the right and left ear in case the spectroscopic device is integrated into headphones. Another example is two positions on the belly, for example in case of a skin patch.

[0043] The spectroscopic measurement at two body locations of the person may be made with the same spectroscopy module, for example two consecutive measurements at different body locations, or with two spectroscopy modules. Hence, the spectroscopic device may comprise two spectroscopy modules arranged such that different body locations of the person can be measured.

[0044] Spectroscopic data may be or may comprise one or more than one spectrum. The term "spectrum” may refer to a data structure in which several intensity values or values derived thereof such as absorbance of radiation are associated with wavelengths or wavelength ranges of the radiation. The wavelength or wavelength ranges may be those described above. The data structure may be a vector, wherein each element represents an intensity and the position in the vector represents a certain wavelength or wavelength range, so the value at a certain position represents the intensity of that wavelength or wavelength range. The data structure may be a vector or matrix containing value pairs, wherein one value represents the wavelength or wavelength range and the other value the intensity at this wavelength or wavelength range. The spectrum recorded by the spectrometer may be corrected by calibration coefficients to compensate for sensor imperfections or drifts. The spectrum may represent the absorbance or transmittance of radiation after having penetrated the skin of the person.

[0045] The spectroscopic device comprises a temperature sensor for acquiring the temperature data of the two body locations of the person. The term "temperature sensor” may refer to sensor which is capable of recording the temperature of the two body locations of the person and convert the temperature into temperature data comprising the value of the temperature. Examples for temperature sensors include thermistors, infrared sensors, integrated circuit temperature sensors, bimetallic strips, liquid crystal temperature sensors, fiber optic temperature sensors, bimetallic coil temperature sensors. The temperature sensor may be placed in close proximity to the spectroscopic module such that the temperature of the body location can be measured for which the spectroscopic data is measured. The term "close proximity” may refer to a distance within which negligible temperature variations can be expected, for example less than 5 cm or less than 1 cm or less than 5 mm. The spectroscopic device may comprise two spectroscopy modules and two temperature sensors, wherein a temperature sensor is arranged in close proximity to each spectroscopy module such that the temperature of both body locations can measured for which spectroscopic data is acquired by the spectroscopy modules. The concentration of a body substance of the person is determined using the spectroscopic data and the temperature data. The spectroscopic data may comprise a first spectrum of a first body location and a second spectrum of a second body location. The temperature data may comprise a first temperature from the first body location and a second temperature of the second body location. The concentration of a body substance of the person may be determined using the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data. The first spectrum may be temperature-corrected by the first temperature. The second spectrum may be temperature-corrected by the second temperature. The temperature correction may be done by a reference indicating temperature-dependent changes of the spectrum, for example shifts or intensity changes.

[0046] The spectroscopic data may be for plausibility, for example by comparing the spectra. If the spectroscopic data does not pass the plausibility check, an error message may be generated, or a new measurement may be triggered. Comparing the spectra may comprise determining if the difference between the spectra is above or below a preset threshold. The plausibility check may be passed if the difference is below the preset threshold. Comparing the spectra may comprise applying a principle component analysis to both spectra and comparing the principle components, for example the two major principle components. The difference between the principle components of the spectra may be determined. The plausibility check may be passed if the difference is below the preset threshold. The plausibility check may involve comparing the spectra to a reference spectrum, for example from a database. The reference spectrum may have been recorded under controlled conditions, for example in the presence of skilled personal or in the context of an enrollment. The plausibility check may be passed if the difference of the spectra of the spectroscopic data and the reference spectrum is below a preset threshold.

[0047] The first spectrum and the second spectrum may be combined into a combined spectrum. The first temperature- corrected spectrum and the second temperature-corrected spectrum may be combined into a combined spectrum. Combining the spectra may be done by averaging or by weighted averaging. Weight-averaging may involve the temperature data, for example spectra recorded in a certain temperature range may be attributed a higher weight than spectra recorded in a different temperature range. The weight of one spectrum may be zero, for example if the temperature is outside a preset temperature range. Weight-averaging may involve a reference spectrum. For example, the closer a spectrum is to a reference spectrum the higher the weight. In this way, false measurements can be excluded without the need for repeating the measurement. The reference spectrum may be an average of spectra measured from different persons or the reference may be a personalized reference for the person from which the spectroscopic data has been obtained. The combined spectrum may be stored in a database. A reference spectrum may be generated from multiple combined spectra stored in the database.

[0048] Determining the concentration of a body substance may involve a reference spectrum, for example a reference spectrum obtained from a database. The combined spectrum may be compared to the reference spectrum. For example, a difference spectrum may be determined by subtracting the reference spectrum from the combined spectrum. Alternatively, the spectra are subject to principle component analysis and the principle components are used for comparison and / or combination. In this way, the influence of background can be reduced. Also, outliers can be identified.

[0049] The concentration of a body substance of the person is determined using the spectroscopic data. The concentration of a body substance of the person may be determined using the spectroscopic data and person data. The concentration of a body substance of the person may be determined using the spectroscopic data and environmental data. The concentration of a body substance of the person may be determined using the spectroscopic data, person data and environmental data. The concentration may be a numeric value, such as mass ratio or a volume ratio. The ratio may relate to the whole body or parts thereof, for example the skin or the blood. For example, in case of alcohol the blood alcohol concentration may be determined. The concentration may be a categoric value, for example indicating the presence of the body substance or certain value ranges, for example none, low, medium, high.

[0050] The term "person data” may refer to data associated with a characteristic of the person such as a physical or chemical characteristic of the person. Person data may refer to any data associated with a characteristic of the person which has been obtained with a method other than spectroscopy. Person data may correlate with the alcohol level of the person. Person data may be personalized data, i.e. specific for a particular person, or it may be data associated with a certain group of people, for example female persons of age 25 to 30. Physical characteristics may comprise thermal characteristics, for example the body temperature, the thermal conductivity or the specific heat capacity of the skin; mechanical characteristics, for example pressure exerted on the spectrometer, compressibility or mechanical elasticity of the skin; optical characteristics, for example the color, refractive index, optical conductivity or absorption coefficients of the skin; electro-magnetic characteristics, for example electrical conductivity, dielectric constant, radio frequency-based permittivity, microwave complex permittivity, millimeter wave complex permittivity, magnetic permittivity or susceptibility of the skin. Chemical characteristics of a person typically refer to the chemical composition of some body tissue like skin, blood or sweat, for example the type and the concentration of certain chemical compounds such as the water content.

[0051] The person data may contain or may be a biomarker. The term "biomarker” may refer to a measurable substance, process or characteristic that is indicative of a biological state or condition. A biomarker may refer to a specific molecule, protein, genetic sequence, or other measurable feature that is associated with a particular disease, condition or treatment response. Examples for biomarkers are body dimensions such as size, head circumference, chest girth, abdominal girth, crotch length, arm length; body weight or body mass index; body topology such as face topology, iris structure, finger print, palm topology; muscle measures like muscular strength, muscular endurance, muscular agility and speed, balance, coordination; cardio-vascular measures such as heart rate, heart rate variability, electrocardiogram, blood pressure, blood oxygen; skin measures such as skin conductance, skin impedance, skin moisture level, skin sebum level, skin roughness, skin elasticity, skin pH, skin blood flow, skin sweat rate; blood metabolites such as blood glucose, blood cholesterol, blood triglycerides, blood urea, blood creatinine, blood lactate, blood bilirubin, blood pH; urine metabolites such as urine glucose, urine urea, urine creatinine, urine ketones, urine pH, urine protein content; hormone levels such as thyroid hormone level, insulin level, growth hormone level, cortisol level, estrogen level, progesterone level, testosterone level, prolactin level; drug levels or levels of drug metabolites such as alcohol, amphetamines, opioids, cocaine, marijuana, benzodiazepines, barbiturates.

[0052] Person data may be received from sensors other than a spectrometer, for example a thermometer, a scale, a balance, an optical camera, an optical 3D scanner system, conductance or impedance gauge such as a corneometer, a sweat rate monitor or sweat patch, a liquid or gas chromatograph, a mass spectrograph, a nuclear magnetic spectrometer or imager, an electrochemical sensor, an immunoassay, a polymerase chain reaction apparatus. The spectroscopic device may be integrated into a portable device which further comprises sensors from which at least parts of the person data is received. Person data may be the temperature measured by the temperature sensor of the spectroscopic device.

[0053] Person data may also be received from a storage device or it can be obtained from a user interface, for example a graphical user interface, to which a user can enter person data, for example from observations. Person data may comprise human characteristics like age, sex, origin, ethnicity; medical history including current and former medications; nutrition such as vegetarian or vegan diet; consumption of stimulants such as caffeine, alcohol, tobacco products, drug; physical activity level such as type of profession, i.e. office job or physically demanding job, kind of sports, average duration of sports, average sleeping hours.

[0054] The term "environmental data” may refer to data associated with a characteristic of the surrounding of the person, for example a physical or chemical characteristic of the surrounding of the person. The characteristic of the surrounding of the person may have an influence on the spectroscopic measurement of the person or on the characteristic of the person such as the physical or chemical characteristic of the person. However, environmental data may not comprise an intrinsic characteristic of the person.

[0055] Environmental data may comprise sensor data from sensors other than a spectrometer. Environmental data may comprise the location of the person, for example the geolocation such as the GPC coordinates, the height above see level, distance to a reference point such as the spectrometer, acceleration, orientation with regard to gravity; weather conditions such air temperature, air pressure, air humidity, wind speed, wind direction, ambient light intensity; time or date; air pollutant levels like CO2 concentration, CO concentration, ozone concentration, nitrogen oxide concentration, sulfur dioxide concentration, fine dust concentration, volatile organic compounds level.

[0056] Sensor data may have been recorded by a sensor capable of determining the sensor data. The sensor may be integrated into the spectrometer. The spectroscopic device may be integrated into a portable device which further comprises sensors from which at least parts of the environmental data is received. The sensor may be communicatively coupled to the spectrometer, for example via a wireless communication or via internet. Examples for sensors may be a GPC receiver, an accelerometer, a gyroscope, an altimeter, a goniometer, a distance sensor like a time-of-flight sensor, a radar or a LIDaR, a pressure sensor such as a MEMS sensor, a piezo sensor or a capacitive sensor, a magnetometer, a barometer, a light sensor, a thermometer, a gas sensor.

[0057] Environmental data may comprise data associated with the spectrometer, for example a spectrometer ID, a version number of the spectrometer, the spectrometer settings, the temperature of the spectrometer, the age of the spectrometer, time since the last calibration was performed, age of the illumination source, number of measurements the spectrometer has already performed in its lifetime or within a certain time such as the last week or the last month. Environmental data may further comprise data associated with the spectroscopic measurement of the person, for example the sampling time, the illumination strength with which the spectrometer illuminates the person, or the distance of the person to the spectrometer.

[0058] Environmental data may be received from a data storage medium. The data storage medium may be part of the spectroscopic device, or it may be a remote storage device, for example a computer system or a cloud system. Environmental data may be received from a database, for example from a database on a remote storage system, in response to a request containing time and / or geographic location. A remote storage system may refer to a system which is far from the person of the measurement, for example a cloud server or a database server. For example, a request containing the GPS coordinates of the person and the time of the spectroscopic measurement may be sent to a cloud server having a weather database. The cloud server may in response to the request send weather data corresponding to the time and location of the request.

[0059] The concentration of a body substance of the person may be determined by employing a chemometric model. The term "chemometric model” may refer to a model which is parameterized to receive spectroscopic data as input and output the concentration of a body substance. The chemometric model may be parameterized to receive spectroscopic data and output the concentration of a body substance. The chemometric model may be parameterized to receive spectroscopic data and environmental data as input and output the concentration of a body substance. The chemometric model may be parameterized to receive spectroscopic data, person data and environmental data as input and output the concentration of a body substance. The chemometric model may be parameterized to receive spectroscopic data as input and output an intermediate concentration of a body substance. The intermediate concentration of a body substance may be adjusted or corrected using the person data and / or the environmental data, for example by employing a refining model. The refining model may be a data-driven model which may be trained with historic data for adjusting or correcting the intermediate concentration of a body substance. A refining model may be a multivariate linear or polynomial regression model, or it may be an artificial neural network.

[0060] A chemometric model may comprise a pre-processing method and a machine learning model to obtain the concentration of a body substance. A chemometric model may comprise a pre-processing method, a feature selection filter and a machine learning model. If the chemometric model comprises two or more partial chemometric models, each partial chemometric model may comprise a separate pre-processing method, a feature selection filter and a machine learning model. Alternatively, the partial models may use the same pre-processing method or feature selection filter.

[0061] The term "pre-processing” may refer to a method to reduce or eliminate interferences from a spectrum such as stray light, noise or baseline drift to enhance the subsequent machine learning. Hence, the pre-processing method may be applied before the machine learning method. Pre-processing may include one or more of baseline correction, scatter correction, smoothing, scaling, aggregation.

[0062] The term "machine learning method” may refer to a model which translates spectra into corresponding person data. The machine learning method hence may use a spectrum as input and derive person data therefrom. The machine learning method may be considered as an integral part of the chemometric model. Machine learning methods may be supervised, semi-supervised or unsupervised. Machine learning methods may include multivariate calibration, classification, pattern recognition, clustering, ensemble methods, neural nets and deep learning, or multivariate curve resolution.

[0063] The term "feature selection filter” may refer to a method to select those parts of the spectrum with a correlation to the person data. A feature selection filter may facilitate the machine learning method of the chemometric model and thus avoid overfitting and reduce the number of required training datasets. A feature selection filter may use a spectrum as input, remove all unselected parts and output a spectrum with only the selected parts left. Hence, the output of the feature selection filter may be a spectrum in form of a vector of lower dimensionality than the input vector. The output of the feature selection filter can be used as input for the machine learning method. Hence, the feature selection filter may be applied before the machine learning method. The input of the feature selection filter may be the received spectrum or it may be the pre-processed spectrum, preferably the pre-processed spectrum. Hence, the feature selection filter may be applied after the pre-processing method.

[0064] A chemometric model may be or may contain a data-driven model. The chemometric model may be a trained data- driven model. Training may comprise adjusting parameters of the chemometric model such that the output of the chemometric model most closely fits to the provided training data. Often, training comprises minimizing a loss or cost function, for example a least mean square value of chemometric model output to provided training data. The complete set of training data may be used for training or parts thereof. Parts of the received training data may be used for training and the remainder may be used for determining the prediction accuracy of the trained chemometric model. Alternatively, cross-validation can be applied, for example K-fold cross-validation, leave-one-out cross- validation, stratified cross-validation. The spectroscopic device may be operatively coupled to a person identification system. The person identification system may provide the identity of the person. The person identification system may be a biometric recognition system, for example a fingerprint recognition system, a hand geometry recognition system, an iris recognition system, a retina recognition system, a face recognition system, a vein recognition system, a voice recognition system. The person identification system may be integrated into the same part of the vehicle as the spectroscopic device or into a different part. For example, the spectroscopic device may be placed behind a display with an integrated fingerprint scanner or a behind-display face recognition system. The person identification system may be used to make sure the person using the spectroscopic device is in fact the person and not a different vehicle passenger. This may be efficiently achieved if the spectroscopic device and the person identification system are in close proximity, for example by integrating both in the same part of the vehicle. Alternatively, or additionally, the personalized person data may be obtained using the identity of the person obtained from a person identification system. In particular, a personalized reference spectrum, i.e. a reference spectrum which is specific for the identified person, may be obtained from a database using the identity of the person. The personalized reference spectrum may be used to determine the concentration of the body substance.

[0065] The face recognition system may be a 2D face recognition system, for example a feature extraction analysis from an image, for example from a RGB or an IR camera. The analysis may yield various features like size and position of eyes, nose, mouth ears and their relative distance and orientation. By comparing such features to a reference database, the identify of the person may be identified.

[0066] The face recognition system may be a 3D face recognition system determining a depth map of the person, for example by a stereo camera system, a structured light system, or a time-of-flight camera system. The depth map may be used to identify the person by comparing it to a reference database. The face recognition system may further comprise material recognition or classification by analyzing characteristic reflection of light from the surface, for example as described in WO 2023 / 156315 A1 .

[0067] The concentration of a body substance determined by the chemometric model may be output. The term "outputting” may refer to writing the concentration of a body substance to a non-transitory data storage medium, for example into a file or database, display it on a user interface, for example a screen, or both. Outputting may further mean to forward the concentration of a body substance to a computer system for further processing, for example an electronic control unit (ECU) or the on-board computer system. It is also possible to output the concentration of a body substance through an interface to a cloud system for storage and / or further processing.

[0068] The concentration of the body substance may be used to determine the person's fitness to drive a vehicle. The processor of the spectroscopic device may be configured to determine the person's fitness to drive a vehicle. The board computer of the vehicle or an ECU may be configured to receive the concentration of the body substance and to determine the person's fitness to drive a vehicle using the concentration of the body substance. The determination may involve determining if the concentration of the body substance exceeds or falls below a threshold. The threshold may be given by law, for example for the blood alcohol concentration or the THC concentration. The threshold may also be specific for a certain group of persons, for example a glucose level for patients suffering from type 1 diabetes. The threshold may be specific for a specific person, i.e. a personal threshold, for example for medial conditions like dehydration which may depend on the specific skin type of a person. Person-specific thresholds may be determined using the person identification described above.

[0069] The concentration of a body substance of the person may be used for controlling a functionality of the vehicle. A control signal may be generated using the concentration of the body substance. The control signal may be usable to control a vehicle access control system, for example to fully exclude a person from using a vehicle with an ignition interlock if the concentration of a body substance is above a threshold or to partially exclude the person if the concentration of a body substance is within a certain range, for example by restricting certain functionalities of a vehicle like the engine power, the maximum achievable speed or the entertainment system.

[0070] The control signal may be a Boolean value indicating whether the access can be granted or not. The control signal may be a numeric value, for example classifier indicating the extent of access which can be granted to the person. The control signal may be generated by determining if the concentration of a body substance is above or below a preset threshold. The determination of the control signal may involve region-specific settings, for example a country or state-specific concentration of a body substance threshold. The region-specific settings may be obtained from a storage medium taking into account the geographic location of the vehicle, for example obtained from a GPS system. The determination of the control signal may involve person data, for example the person's age to determine an agespecific threshold of blood alcohol concentration. The determination of the control signal may involve personalized person data, for example a personalized threshold of blood alcohol concentration which may be lower than the general threshold, for example due to a court order as a consequence of a prior driving under the influence. Personalized person data may be selected from a database using the person identity obtained from person identification as described above.

[0071] The control signal may be used for geofence lockout, for example prevent the vehicle from leaving a designated area, for example a home or highways, if a preset concentration of a body substance is exceeded; for passive alert, for example discreetly notify emergency contacts or roadside assistance if a preset concentration of a body substance is exceeded; for adapting autonomous driving functionality, for example, increase distance kept to vehicles driving in front and increase break system pressure to allow for more effective breaking and avoid accidents due to reduced reaction time if the concentration of a body substance is within a preset range; for data logging, for example maintain a discreet log of concentration of a body substance readings for personal health tracking or potential use by law enforcement; for determining eligibility, for example restrict driving privileges based on concentration of a body substance for individuals with prior driving under the influence convictions or for novice persons; for insurance premium adjustments, for example to adjust insurance premiums based on concentration of a body substance measurement history to encourage responsible driving behavior; for emergency response decisions, for example to improve decision making of law enforcement and medical personnel, taking into account concentration of a body substance levels of individuals involved in accidents or medical emergencies; for real-time fleet monitoring, for example an alert fleet managers to elevated concentration of a body substance readings, allowing for immediate intervention such as contacting the person, dispatching a replacement, in particular for commercial vehicles; for route restriction, for example automatically reroute vehicles driven by someone with a detected concentration of a body substance to avoid high-risk areas or congested roads; for remote engine disable, for example in extreme cases such as very high concentration of a body substance to allow fleet managers to remotely disable the vehicle to prevent accidents; for person rewards or penalties, for example to implement incentive programs for maintaining clear person records and penalties for violations.

[0072] The present disclosure further relates to a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to the present disclosure. The term "computer-readable data medium” may refer to any suitable data storage device or computer readable memory on which is stored one or more sets of instructions (for example software) embodying any one or more of the methodologies or functions described herein. The instructions may also reside, completely or at least partially, within the main memory and / or within the processor during execution thereof by the computer, main memory, and processing device, which may constitute computer-readable storage media. The instructions may further be transmitted or received over a network via a network interface device. Computer-readable data medium include hard drives, for example on a server, USB storage device, CD, DVD or Blue-ray discs. The computer program may contain all functionalities and data required for execution of the method according to the present disclosure or it may provide interfaces to have parts of the method processed on remote systems, for example on a cloud system.

[0073] Brief Description of the Figures

[0074] Figure 1 illustrates an example for a spectroscopic device.

[0075] Figure 2 shows an example for the placement of the spectroscopic device in the steering wheel of a vehicle.

[0076] Figure 3 shows an example for the placement of the spectroscopic device in a headphone.

[0077] Figure 4 shows an example of processing spectroscopic data and temperature data.

[0078] Figure 5 shows another example of processing spectroscopic data and temperature data. Figure 6 shows two examples of how the concentration of a body substance can be determined from spectroscopic data, person data and / or environmental data.

[0079] Description of Embodiments

[0080] Figure 1 illustrates an example for a spectroscopic device. The spectroscopic device may be suitable to acquire spectroscopic data of the person from its finger 120. Between the spectroscopic module 100 and the finger 120, there may be a cover 110 which is at least partially transparent to the light emitted by the light emitting element 102. The cover 110 may be a sheet of glass or a polymer like polycarbonate or polymethyl methacrylate (PMMA). The cover 110 may also be a transparent display, for example the display of a control panel or a multimedia system. The spectroscopic device may comprise a temperature sensor 111 , for example a thermocouple or a resistance temperature detector. The temperature sensor 111 may be placed between the cover 110 and the finger 120, so the finger is in contact with the temperature sensor. Alternatively, the temperature sensor 111 may be placed behind the cover 110, for example in case the temperature sensor 111 needs no direct contact, such as a infrared temperature sensor, or the cover 110 has sufficient thermal conductivity. The temperature sensor may be communicatively coupled with a processor, for example the processor 107 of the spectrometer module, so the temperature data can be read out.

[0081] The spectroscopic device may comprise a spectroscopic module 100. The spectroscopic module 100 may comprise a substrate 101 , for example a printed circuit board (PCB). The spectroscopic module 100 may comprise a light emitting element 102, for example an LED. The LED may emit light of a desired wavelength, for example infrared light in the range of 750 nm to 2.5 m. The light emitting element 102 may emit a light ray 103 directed towards the finger 120 of the person.

[0082] The spectroscopic module 100 may comprise a set of photosensors 104 which may be mounted on the substrate 101 . The set of photosensors 104 may comprise an array of photosensors, for example a 3 times 3 array. Each photosensor 105 may be sensitive to light at the wavelength range emitted by the light emitting element 102. The light ray 103 may impinge on the set of photosensors 104 after having penetrated into the finger 120. Each photosensor 105 may be covered with an optical filter 106. The optical filters 106 may be chosen to let pass light at different wavelengths, so each photosensor 105 receives a different wavelength range of light. The photosensor 105 may comprise a photosensitive material, for example a photoconductor like lead sulfide (PbS). The photosensor may generate an electric signal depending on the light intensity of the light impinging on the photosensor 105.

[0083] The spectroscopic module 100 may comprise a processor 107 which may be mounted on the substrate 101. The processor 107 may be operatively coupled to the light source 102 and the photodetectors 105, for example via electric conductors on the PCB. The processor 107 may be a microcontroller configured to control the light emitting element 102, for example to switch it on during the measurement and switch it off afterwards. The processor 107 may be a microcontroller configured to receive the electric signal from the photosensors 105 and convert them into digital signals by analog-to-digital conversion. The processor may thus generate spectroscopic data which may either be further processed to determine the concentration of a body substance of the person or it may forward the spectroscopic data to an electronic control unit (ECU) of the vehicle or the on-board computer of the vehicle for determining the concentration of a body substance of the person.

[0084] Figure 2 shows an example for the placement of the spectroscopic device in the steering wheel of a vehicle. The steering wheel 201 may be in a car and configured to measure the fitness of the person to drive, for example the blood alcohol concentration of the person. The spectroscopic device comprises a first part 210 comprising a first temperature sensor 211 and a first spectroscopy module 212. The spectroscopic device comprises a second part 220 comprising a second temperature sensor 221 and a second spectroscopy module 222. The person may touch the steering wheel 201 , so the first part 210 may be in contact with the left hand of the person and the second part 220 may be in contact with the right hand of the person. Hence, the first temperature sensor 211 may measure the temperature of the left hand of the person and the first spectroscopy module 212 may measure a spectrum of the left hand of the person. The second temperature sensor 211 may measure the temperature of the right hand of the person and the second spectroscopy module 212 may measure a spectrum of the right hand of the person.

[0085] Figure 3 shows an example for the placement of the spectroscopic device in a headphone. The headphone 301 may be mounted on the head of a person 302. The headphone may comprise a first temperature sensor 311 , a first spectroscopy module 312, a second temperature sensor 321 and a second spectroscopy module 322. The first temperature sensor 311 may measure the temperature of the left ear of the person and the first spectroscopy module 312 may measure a spectrum of the left ear of the person. The second temperature sensor 311 may measure the temperature of the right ear of the person and the second spectroscopy module 312 may measure a spectrum of the right ear of the person.

[0086] Figure 4 shows an example of processing spectroscopic data and temperature data. Spectroscopic data may comprise a first spectrum 411 measured on a first body location 410, for example the left palm or the left ear, and a second spectrum 421 measured on a second body location 420, for example the right palm or the right ear. Temperature data may comprise a first temperature 412 measured on the first body location 410 and a second temperature 422 measured on the second body location 420. Spectroscopic data and temperature data may be measured with the same spectrometer module and temperature sensor or with two separate ones. The first spectrum 411 may be temperature-corrected using the first temperature 412 to obtain a temperature-corrected first spectrum 413. The second spectrum 421 may be temperature-corrected using the second temperature 422 to obtain a temperature-corrected second spectrum 423. Temperature correction may involve shifting the spectra according to the temperature and / or adjusting the intensity according to the temperature.

[0087] The temperature-corrected spectra 413 and 423 may be combined to obtain a combined spectrum 430. Combining may be averaging the spectra 413 and 423, weight-averaging, for example based on a comparison to a reference, or selecting one of the spectra 413 and 423, for example based on a comparison to a reference. The combined spectrum 430 may be input into a chemometric model 440 which outputs the concentration of a body substance 450, for example the blood alcohol concentration or a glucose concentration.

[0088] Figure 5 shows another example of processing spectroscopic data and temperature data. A combined spectrum 511 may be obtained by combining temperature-corrected spectra 501 and 502, for example as described in the example of figure 4. The combined spectrum 511 may be stored to a database 520, for example in a memory of the spectroscopic device or a cloud service which may be communicatively coupled with the spectroscopic device. The combined spectrum 511 may be stored to a database 520 together with a time stamp. The combined spectrum 511 may be compared with a reference spectrum 512. For example, a difference spectrum 513 may be generated by subtracting the reference spectrum 512 from the combined spectrum 511 . The difference spectrum 513 may comprise less of background, for example typical skin components like water or fat, so the signal of interest has a higher weight in the difference spectrum 513.

[0089] The reference spectrum 512 may be obtained from database 520. The reference spectrum 512 may be a spectrum which has been recorded under controlled conditions, for example with a parallel measurement of the body substance of interest with method different from spectroscopy. The reference spectrum 512 may be an average spectrum from multiple persons or it may be obtained from the same person the combined spectrum 511 is from. Such personalized spectrum may stem from an enrollment process which establishes controlled conditions. The reference spectrum 512 may also be an average of combined spectra 511 from former measurements, for example a weight average, wherein more recent spectra have a higher weight than older spectra to account for slow changes, for example due to aging or due to the season. The difference spectrum 513 may be input into a chemometric model 530 which outputs the concentration of body substance 540.

[0090] Figure 6 shows two examples of how the concentration of a body substance can be determined from spectroscopic data, person data and / or environmental data. In Figure 6a spectroscopic data 611 may be input to a chemometric model comprising pre-processing 621, feature selection 622 and a machine learning model 623. Spectroscopic data 611 may comprise a spectrum, for example a near infrared spectrum, obtained from a measurement with a spectrometer. Pre-processing 621 may comprise baseline correction, for example first-order derivation, scatter correction, for example standard normal variate, smoothing, for example moving average filtering, scaling, for example Pareto scaling, aggregation, for example spatial median. The pre-processed spectroscopic data may subsequently undergo feature selection 622. Feature selection 622 may reduce the dimensionality of the spectroscopic data 611, so training the machine learning model 623 requires less training data. Feature selection 622 may for example involve principle component regression (PCR). The thus pre-processed and feature-selected spectroscopic data may be passed as input to a machine leaning model 623, for example an artificial neural network. The machine leaning model 623 may be parametrized to further receive the person data 612 and the environmental data 613 as further input. The person data 612 and the environmental data 613 may be pre-processed before inputting into the machine learning model 613, for example to adjust the format and the units of the data. The machine leaning model 623 may be trained with historic data comprising spectroscopic data, person data and environmental data. The machine leaning model 623 may output the concentration of a body substance 615, for example its quantity or concentration. Using a chemometric model which uses spectroscopic data, person data and environmental data as input has the advantage that complex interplays between spectroscopic data, person data and environmental data can be taken into account.

[0091] Figure 6b shows an alternative example for determination of a concentration of a body substance from spectroscopic data, person data and / or environmental data. Spectroscopic data 611 may be input to a chemometric model 631 which outputs an intermediate concentration of a body substance 632. The chemometric model 631 may not be parametrized to take person data 612 and / or environmental data 613 into account. Hence, the intermediate concentration of a body substance 632 only depends on the spectroscopic data 632. In order to obtain the desired concentration of a body substance 634, a refining model 633 may be employed. The refining model 633 may be parametrized to receive the intermediate concentration of a body substance 632, the person data 612 and the environmental data 613 and to output the concentration of a body substance 634. The refining model 633 may comprise to sub-models, one which processes the person data 612 and one which processes the environmental data 613. For example, the first sub-model may receive the intermediate concentration of a body substance 632 and the person data 612 as input and output a refined concentration of a body substance. A second sub-model may use the refined concentration of a body substance and the environmental data 613 as input and output the concentration of a body substance 634. The refining model 633 may be a multivariate polynomial regression model which adjusts the intermediate concentration of a body substance 632 according to the person data 612 and the environmental data 613 to arrive at the concentration of a body substance 634. A refining model 633 has the advantage that the chemometric model 631 does not need a retraining for new person data types or environmental data types.

[0092] The present disclosure has been described in conjunction with preferred embodiments and examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed disclosure, from the studies of the drawings, this disclosure and the claims.

[0093] Any steps presented herein can be performed in any order. The methods disclosed herein are not limited to a specific order of these steps. It is also not required that the different steps are per-formed at a certain place or in a certain computing node of a distributed system, i.e. each of the steps may be performed at different computing nodes using different equipment / data processing.

[0094] As used herein ..determining" also includes ..initiating or causing to determine", "generating" also includes ..initiating and / or causing to generate" and "providing” also includes "initiating or causing to determine, generate, select, send and / or receive”. "Initiating or causing to perform an action” includes any processing signal that triggers a computing node or device to perform the respective action. In the claims as well as in the description the word "comprising” does not exclude other elements or steps and the indefinite article "a” or "an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation. In the claims as well as in the description the word "comprising” or "including” or similar wording does not exclude other elements or steps and shall not be construed limiting to the elements or steps lined out. The indefinite article "a” or "an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation or further elements may be included.

[0095] Providing in the scope of this disclosure may include any interface configured to provide data. This may include an application programming interface, a human-machine interface such as a display and / or a software module interface. Providing may include communication of data or sub-mission of data to the interface, in particular display to a user or use of the data by the receiving node, entity or interface.

[0096] Various units, circuits, entities, nodes or other computing components may be described as "configured to” perform a task or tasks. Configured to shall recite structure meaning "having circuitry that” performs the task or tasks on operation. The units, circuits, entities, nodes or other computing components can be configured to perform the task even when the unit / circuit / component is not operating. The units, circuits, entities, nodes or other computing components that form the structure corresponding to "configured to” may include hardware circuits and / or memory storing program instructions executable to implement the operation. The units, circuits, entities, nodes or other computing components may be described as performing a task or tasks, for convenience in the description. Such descriptions shall be interpreted as including the phrase "configured to.” Any recitation of "configured to” is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation.

[0097] In general, the methods, apparatuses, systems, computer elements, nodes or other computing components described herein may include memory, software components and hardware components. The memory can include volatile memory such as static or dynamic random-access memory and / or nonvolatile memory such as optical or magnetic disk storage, flash memory, programmable read-only memories, etc. The hardware components may include any combination of combinatorial logic circuitry, clocked storage devices such as flops, registers, latches, etc., finite state machines, memory such as static random-access memory or embedded dynamic random-access memory, custom designed circuitry, programmable logic arrays, etc.

[0098] Any disclosure and embodiments described herein relate to the methods, the systems, apparatuses, devices, chemicals, materials, computer program elements lined out above and vice versa. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa. All terms and definitions used herein are understood broadly and have their general meaning.

Claims

Claims1 . A spectroscopic device for determining a concentration of a body substance of a person comprising: a) a spectroscopy module for acquiring spectroscopic data measured at two body locations of the person, wherein the spectroscopic data comprises a first spectrum of a first body location and a second spectrum of a second body location, b) a temperature sensor for acquiring temperature data measured at the two body locations of the person, wherein the temperature data comprises a first temperature from the first body location and a second temperature of the second body location, c) a processor for determining the concentration of the body substance of the person using the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data, and d) an output for outputting the concentration of the body substance of the person.

2. The spectroscopic device according to claim 1 , wherein the spectroscopic device comprises two spectroscopy modules and two temperature sensors, wherein a temperature sensor is arranged in close proximity to each spectroscopy module.

3. The spectroscopic device according to claim 1 or 2, wherein the two body locations are at least 10 cm apart from each other.

4. The spectroscopic device according to any of the claims 1 to 3, wherein the processor is configured to determine the concentration of the body substance using a reference spectrum obtained from a database.

5. The spectroscopic device according to any of the claims 1 to 4, wherein the spectroscopic device comprises a person identification system to provide the identity of the person.

6. The spectroscopic device according to claim 5, wherein the processor is configured to determine the concentration of the body substance using a personalized reference spectrum obtained from a database using the identity of the person.

7. The spectroscopic device according to any of the claims 1 to 6, wherein the spectroscopy module comprises a photosensor configured for measuring optical radiation with a wavelength of 750 nm to 2.5 pm.

8. The spectroscopic device according to any of the claims 1 to 7, wherein the spectroscopy module comprises an array of photosensors, wherein an optical filter element is positioned in a light path before each photosensor.

9. A vehicle comprising the spectroscopic device according to any of the claims 1 to 8.

10. The vehicle according to claim 9, wherein the spectroscopic device is integrated into the steering wheel of the vehicle.11 . A method for determining a concentration of a body substance of a person comprising: a) receiving spectroscopic data of the person measured at two body locations of the person, wherein the spectroscopic data comprises a first spectrum of a first body location and a second spectrum of a second body location, b) receiving temperature data measured at the two body locations of the person, wherein the temperature data comprises a first temperature from the first body location and a second temperature of the second body location, c) determining the concentration of the body substance of the person using the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data, and d) outputting the concentration of the body substance of the person.

12. The method according to claim 11, wherein the body substance is an intoxicant or its metabolite.

13. The method according to claim 11 or 12, wherein the concentration of the body substance of the person is determined using person data associated with a characteristic of the person or environmental data associated with a characteristic of the surrounding of the person.

14. Use of the concentration of the body substance of the person obtained from the method of any of the previous claims for determining the person's fitness to drive a vehicle.

15. A non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: a) receiving spectroscopic data of the person measured at two body locations of the person, wherein the spectroscopic data comprises a first spectrum of a first body location and a second spectrum of a second body location, b) receiving temperature data measured at the two body locations of the person, wherein the temperature data comprises a first temperature from the first body location and a second temperature of the second body location, c) determining the concentration of the body substance of the person using the the first spectrum and the second spectrum of the spectroscopic data and the first temperature and the second temperature of the temperature data, andd) outputting the concentration of the body substance of the person.

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