CONTACTLESS AND MINIMAL-CONTACT MONITORING OF QUALITY OF LIFE PARAMETERS FOR ASSESSMENT AND INTERVENTION

Abstract

An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

Claims

1. A system comprising: a stand-alone unit comprising: a non-contact sensor configured to generate measured data based on signals reflected from a subject; an interface for collecting quality of life (QOL) data from the subject; and one or more processors configured to (i) generate a sleep quality of life (SQOL) parameter on the basis of the measured data generated by the non-contact sensor and the QOL data collected from the interface or (ii) transmit the measured data and the QOL data to a remote data monitoring center for calculating a SQOL parameter on the basis of the measured data and the QOL data transmitted to the remote data monitoring center.

2. The system of claim 1, wherein the SQOL parameter is derived from a combination of objective data and subjective data, wherein the objective data is derived from the measured data generated by the non-contact sensor, and wherein the subjective data is derived from the QOL data collected from the interface.

3. The system of claim 2, wherein the objective data comprises a total sleep time value, a sleep efficiency value, a sleep onset latency value, a number of periods of objectively measured wakefulness greater than 1 minute, or a wake-after-sleep-onset value.

4. The system of claim 3, wherein the subjective data comprises a subjective assessment of sleep duration, sleep efficiency, number of awakenings, or sleep latency.

5. The system of claim 1, wherein the SQOL parameter is a sleep duration index, a sleep fragmentation index, or a sleep latency index.

6. The system of claim 1, wherein the SQOL parameter is a sleep duration index derived from a combination of an objective assessment of sleep duration and a subjective assessment of sleep duration, wherein the objective assessment of sleep duration is derived from the measured data generated by the non-contact sensor, and wherein the subjective assessment of sleep duration is derived from the QOL data collected from the interface.

7. The system of claim 1, wherein the SQOL parameter is a sleep fragmentation index derived from a combination of an objective number of awakenings and a reported number of awakenings, wherein the objective number of awakenings is derived from the measured data generated by the non-contact sensor, and wherein the reported number of awakenings is derived from the QOL data collected from the interface.

8. The system of claim 1, wherein the SQOL parameter is a sleep latency index derived from a combination of an objective sleep latency value and a subjective sleep latency value, wherein the objective sleep latency value is derived from the measured data generated by the non-contact sensor, and wherein the subjective sleep latency value is derived from the QOL data collected from the interface.

9. The system of claim 1, wherein the non-contact sensor is a biomotion sensor and the reflected signals are radio-frequency signals.

10. The system of claim 1, wherein the interface comprises a microphone for collecting the QOL data from the subject.

11. The system of claim 1, wherein the stand-alone unit further comprises a microphone configured to detect a sound parameter, and wherein the SQOL parameter is calculated on the basis of the measured data, the QOL data, and the sound parameter.

12. The system of claim 1 further comprising: an environmental sensor configured to detect an environmental parameter, wherein the SQOL parameter is calculated on the basis of the measured data, the QOL data, and the environmental parameter.

13. The system of claim 12, wherein the environmental parameter is an environmental temperature or a light level.

14. The system of claim 1 further comprising: the remote data monitoring center, wherein the one or more processors are configured to transmit the measured data and the QOL data to the remote data monitoring center for calculating the SQOL parameter.

15. The system of claim 14, wherein the stand-alone unit further comprises a receiver configured to receive, from the remote data monitoring center, a behavioral recommendation for the subject, and wherein the behavioral recommendation is based on the SQOL parameter.

16. The system of claim 1 further comprising: a feedback device configured to: receive, from the stand-alone unit or the remote data monitoring center, a behavioral recommendation for the subject, wherein the behavioral recommendation is based on the SQOL parameter; and provide the behavioral recommendation to the subject.

17. The system of claim 16, wherein the behavioral recommendation pertains to an increase or a decrease of an allowed sleep time of the subject.

18. The system of claim 16, wherein the behavioral recommendation pertains to an increase or a decrease of an extended awakening time of the subject.

19. A method comprising: receiving, at a stand-alone unit, measured data generated by a non-contact sensor from signals reflected from a subject; collecting, via an interface of the stand-alone unit, quality of life (QOL) data from the subject; and either (i) processing, via the stand-alone unit, the measured data generated by the non-contact sensor and the QOL data collected via the interface to calculate a sleep quality of life (SQOL) parameter on the basis of the measured data and the QOL data, or (ii) transmitting, via the stand-alone unit, the measured data and the QOL data to a remote data monitoring center for calculating a SQOL parameter on the basis of the measured data and the QOL data transmitted to the remote data monitoring center.

20. The method of claim 19 further comprising: receiving, at a feedback device, a behavioral recommendation for the subject from the stand-alone unit or the remote data monitoring center, wherein the behavioral recommendation is based on the SQOL parameter.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Embodiments of the disclosure will now be described with reference to the accompanying drawings in which:

[0023] FIG. 1 is a diagram illustrating an overall schematic of an embodiment;

[0024] FIG. 2 is a specific example of an embodiment in which a contactless sensor is used to monitor the sleeping state of a subject, by placement in a nearby location (bedside locker);

[0025] FIG. 3 is an example of an input device embodiment that could be used to capture subjective responses from individuals;

[0026] FIG. 4 is an alternative example of an embodiment in which a web-site could be used to capture the subjective responses from an individual;

[0027] FIGS. 5A and 5B show representations of some of the raw data captured by a specific contactless sensor used in a sleep trial based on an embodiment of this disclosure;

[0028] FIG. 6 shows example results of the system in measuring sleep apnea in clinical trial; and

[0029] FIGS. 7A and 7B show schematic representations of behavioral interventions based on one or more embodiments of this disclosure.

DETAILED DESCRIPTION

[0030] FIG. 1 is a diagram illustrating an overall schematic of an embodiment of this disclosure. Monitored subject 101 may be observed by a plurality of contactless 102 and minimal contact sensors 103. Subject 101 may also has access to input device 104 capable of obtaining subjective feedback from the subject through written text or recorded sound. Data aggregation and transmission device 105 collects the data from the sensors 102, 103 and 104, and may also control data sampling and input parameters used by the various sensors and devices. Optionally, display/feedback device 107 can be provided to the local user (e.g., this might indicate whether a signal is being collected from them, or give feedback on the most recent set of QOL parameters measured). Data aggregation and transmission device 105 may be configured to communicate in a bilateral way with remote data archiving and analysis system 106. Data archiving and analysis system 106 may store data from a plurality of subjects, and can carry out analysis of the recorded signals and feedback. It may also communicate with data display device 107 which can show the results of the analysis to a user, or with an optional separate display device 108 which shows the QOL parameters to a remote user.

[0031] FIG. 2 illustrates an embodiment of a contactless sensor that objectively monitors the sleeping state of a subject. In this embodiment, the sensor unit may contain one or more of a radio-frequency based biomotion sensor, a microphone (to pick up ambient sound), a temperature sensor (to pick up ambient temperature), a light sensor (to pick up ambient light levels), and an infrared detector for measuring the subject temperature. The contactless sensor may be placed on a bedside table, for example.

[0032] FIG. 3 illustrates an example of an embodiment of an input device for collecting user input. The input device would typically include alphanumeric keypad 301, display 302, microphone 303, and loudspeaker 304. This allows the generation of questions using either visual or audio means, and a person can then answer the questions using either text or audio input.

[0033] FIG. 4 illustrates an embodiment using a personal computer with an internet browser to capture subjective perceptions of sleep.

[0034] FIGS. 5A and 5B provide examples of raw signals captured using a contactless sensor in a trial for measuring sleep quality-of-life. FIG. 5A shows the signal when a person is asleep and then turns over on their side. FIG. 5B shows the signal when the person is in deep sleep.

[0035] FIG. 6 is an example of how the contactless system can estimate apnea-hypopnea index in a clinical trial with an accuracy similar to that of the current polysomnogram (PSG) estimates.

[0036] FIG. 7 is an example of a behavioral intervention based on use of the system to enhance sleep quality. FIG. 7(A) shows the components of a intervention based over several weeks, in which there is an initial session at which detailed information about sleep is provided, and the person is given the system for measurement of their sleep quality-of-life index (SQOLI).

[0037] FIG. 7(B) shows an example of a specific algorithm that could be used within the intervention, based on the feedback from the SQOLI monitoring. For example, if they achieve an SQOLI greater than target, they can increase their time in bed by 30 minutes. If they fail, they can reduce time in bed by 15 minutes.

[0038] A typical embodiment of a system of this disclosure may include one or more non-contact sensors or minimal-contact sensors that can include one or more of the following: [0039] (a) A biomotion sensor which measures movement, and which derives respiration, heart rate and movement parameters. An example of such a sensor is more fully described in the article written by P. de Chazal, E. O'Hare, N. Fox, C. Heneghan, “Assessment of Sleep/Wake Patterns Using a Non-Contact Biomotion Sensor”, Proc. 30th IEEE EMBS Conference, August 2008, published by the IEEE, the entire contents of which are incorporated herein by reference. In one embodiment, the biomotion sensor may use a series of radio-frequency pulses at 5.8 GHz to generate echoes from a sleeping subject. The echoes may be mixed with the transmitted signals to generate a movement trace which includes movements due to breathing, heart rate, and positional changes. [0040] (b) An audio sensor which measures ambient sound. A specific example of a microphone appropriate for inclusion in the system would be the HK-Sound Omni, −27 dB microphone with part number S-OM9765C273S-C08. [0041] (c) A temperature sensor which measures environmental temperature (typically to ±1C). A specific example of a temperature sensor appropriate for inclusion would be the National Semiconductor LM20, SC70 package. [0042] (d) A light level sensor would measure light level. A specific example of a light level sensor appropriate for inclusion is the Square Clipsal Light-Level Sensor. [0043] (e) A body-temperature measuring sensor. A specific example of a sensor that may be used in the system is the body thermometer Part No. 310 from the Yuan Ya Far Asia Company.

[0044] The minimal contact sensors may include one or more of the following: [0045] (a) A weighing scales for measuring body weight. A specific example is the A&D UC-321PBT. [0046] (b) A blood pressure device, such as the A&F UA767PBT. [0047] (c) A continuous positive airway pressure device for treating sleep apnea, such as the ResMed Autoset Spirit S8. [0048] (d) A pedometer for measuring step-counts (such as the Omron Pocket Pedometer with PC software, HJ-7201TC). [0049] (e) A body-worn accelerometer for measuring physical activity during the day (such as the ActivePAL device). [0050] (f) A body composition analyzer such as the Omron Body Composition Monitor with Scale, HBF-500, which calculates visceral fat and base metabolic rate. [0051] (g) Other contactless or minimally contacting devices could also be included.

[0052] In one or more embodiments, the system may include a data-acquisition and processing capability which provides a logging capability for the non-contact and minimal-contact sensors described above. This typically could include, for example, an analog-to-digital converter (ADC), a timer, and a processor. The processor may be configured to control the sampling of the signals, and may also apply any necessary data processing or reduction techniques (e.g., compression) to minimize unnecessary storage or transmission of data.

[0053] A data communication subsystem may provide communication capability which could send the recorded data to a remote database for further storage and analysis, and a data analysis system including, for example, a database, can be configured to provide processing functionality as well as input to a visual display.

[0054] In one specific embodiment of the system, data acquisition, processing, and communications can utilize using, for example, a Bluetooth-enabled data acquisition device (e.g. the commercially available BlueSentry® device from Roving Networks). Other conventional wireless approaches may also be used. This provides the ability to sample arbitrary voltage waveforms, and can also accept data in digital format.

[0055] In this embodiment, the Bluetooth device can then transmit data to a cell phone using the Bluetooth protocol, so that the data can be stored on a cell-phone memory. The cell phone can also carry out initial processing of the data. The cell phone can also be used as a device for capturing subjective data from the user, using either a text-based entry system, or through a voice enabled question-and-answer system. Subjective data can also be captured using a web-page.

[0056] The cell phone can provide the data transmission capability to a remote site using protocols such as GPRS or EDGE. The data analysis system is a personal computer running a database (e.g., the My SQL database software), which is capable of being queries by analysis software which can calculate useful QOL parameters. Finally a data display capability can be provided by a program querying the database, the outputs of the analytical program and using graphical or text output on a web browser.

[0057] As an example of the clinical use of a specific embodiment, the system was used to measure quality-of-life related to sleep in a specific clinical trial scenario. A group of 15 patients with chronic lower back pain (CLBP), and an age and gender matched cohort of 15 subjects with no back pain were recruited. After initial screening and enrollment, study participants completed a baseline assessment. Gender, age, weight, height, BMI and medication usage were recorded. All subjects completed baseline self report measures of sleep quality (Pittsburgh Sleep Quality Index Insomnia Severity Index [16], quality of life (SF36v2) [17] and pain as part of the SF36v2 questionnaire (bodily pain scale of the SF36v2). The CLBP subjects also completed the Oswestry Disability Index (ODI) as a measure of functional disability related to their low back pain. All subjects then underwent two consecutive nights of objective monitoring using the non-contact biomotion sensor mentioned above, while simultaneously completing a subjective daily sleep log; the Pittsburgh Sleep Diary. Table 1 shows some objective measurements of sleep using the system, and includes the total sleep time, sleep efficiency, sleep onset latency. Other objective parameters which could be measured would include: number of awakenings (>1 minute in duration) and wake-after-sleep-onset.

TABLE-US-00001 TABLE 1 Objective sleep indices obtained using the system Control Group CLBP Group Variable (mean ± sd) (mean ± sd) p-value Total sleep time (mins) 399 (41) 382 (74) 0.428 Sleep Efficiency (%) 85.8 (4.4) 77.8 (7.8) 0.002 Sleep Latency (mins) 9.4 (10.2) 9.3 (11.1) 0.972

[0058] The objective sleep indices described in Table 1 were obtained using a sleep stage classification system that processed the non-contact biomotion sensor data to produce sleep and awake classifications every 30 seconds. This was developed using the following observations:

[0059] Large movements (e.g., several em in size) can be easily recognized in the non-contact signal. Bodily movement provides significant information about the sleep state of a subject, and has been widely used in actigraphy to determine sleep/wake state. The variability of respiration changes significantly with sleep stage. In deep sleep, it has long been noted that respiration is steadier in both frequency and amplitude than during wakefulness of REM sleep.

[0060] Accordingly, a first stage in processing of the non-contact biomotion signal was to identify movement and respiration information. To illustrate how this is possible, FIG. 5A shows an example of the signal recorded by the non-contact sensor when there is a significant movement of the torso and arms due to the person shifting sleeping position. An algorithm based on detection of high amplitude and frequency sections of the signal was used to isolate the periods of movement.

[0061] For periods where there is no significant limb or torso movement, respiratory-related movement is the predominant recorded signal and estimates of breathing rate and relative amplitude are obtained using a peak and trough identifying algorithm. FIG. 5B illustrates the signal recorded by the sensor during a period of Stage 4 sleep that demonstrates a steady breathing effort.

[0062] To validate the performance of the system in correctly labeling 30-second epochs, we recorded signals simultaneously with a full polysomnogram (PSG) montage. We compared the sleep epoch annotations from the PSG and the non-contact biomotion sensor and report the overall classification accuracy, sleep sensitivity and predictivity, wake specificity and predictivity. The overall accuracy is the percentage of total epochs correctly classified. The results are shown in Table 2, and provide evidence that the system can objectively measure sleep with a high degree of accuracy.

TABLE-US-00002 TABLE 2 Accuracy of objective recognition of sleep state using the contactless method Overall By sleep state Awake 69% Awake 69% Sleep 87% REM 82% Pred. of Awake 53% Stage 1 61% Pred. of Sleep 91% Stage 2 87% Accuracy 82% Stage 3 97% Stage 4 98%

[0063] Table 3 shows some of the subjective measurements from the same subjects, and includes their subjective assessment of sleep duration, sleep efficiency, number of awakenings, and sleep latency for each night, as well as their overall PSQI and ISI scores.

TABLE-US-00003 TABLE 3 Subjective sleep indices obtained using the system Control Group CLBP Group Variable (mean ± sd) (mean ± sd) p-value Pittsburgh Sleep Quality Index 2.1 (2.1) 11.7(4.3) <0.001 Insomnia Severity Index 2.8 (4.6) 13.4 (7.3) <0.001 Estimated Sleep Onset Latency 11.7(4.3) 45.3 (27.7) <0.001 Estimated Sleep Efficiency 95.3 (5.8) 73.4 (16.5) <0.001 Estimated Night Time Awakenings 2/15 15/ <0.001

[0064] The system can report these subjective and objective measurements of sleep but, in one aspect, it can also report parameters related to overall Sleep Quality of Life Index (SQOLI) which combines objective and subjective measurements. There are a number of ways in which this could be done. For example, we could define the following SQOL indices:

[00001]$\mathrm{SQOL}.Math..Math.\mathrm{duration}=\left\{0.8\times \mathrm{OBJECTIVE}.Math..Math.\mathrm{SLEEP}.Math..Math.\mathrm{DURATION}+0.2\times \mathrm{SUBJECTIVE}.Math..Math.\mathrm{SLEEP}.Math..Math.\mathrm{DURATION}\right\}$$\mathrm{SQOL}.Math..Math.\mathrm{fragmentation}=\{\left(\mathrm{number}.Math..Math.\mathrm{of}.Math..Math.\mathrm{periods}.Math..Math.\mathrm{of}.Math..Math.\mathrm{objectively}.Math..Math.\mathrm{measured}.Math..Math.\mathrm{wakefulness}>1.Math..Math.\mathrm{minute}+\mathrm{reported}.Math..Math.\mathrm{self}.Math..Math.\mathrm{awakenings}\right)/\mathrm{objective}.Math..Math.\mathrm{sleep}.Math..Math.\mathrm{duration}.Math.\}$$\mathrm{SQOL}.Math..Math.\mathrm{latency}=\sqrt{\begin{array}{c}\mathrm{OBJECTIVE}.Math..Math.\mathrm{SLEEP}.Math..Math.\mathrm{LATENCY}\times \\ \mathrm{SUBJECTIVE}.Math..Math.\mathrm{SLEEP}.Math..Math.\mathrm{LATENCY}\end{array}}$

[0065] The skilled user will be able to construct other combined measurements of sleep quality of life which capture the most meaningful outcomes for a particular application.

[0066] In another embodiment, the system may be used to capture quality-of-life in patients with chronic cough (e.g., patients suffering from chronic obstructive pulmonary disease). In this embodiment, two contactless sensors may be used: the non-contact biomotion sensor described above, and a microphone. The system can measure objectively sounds associated with each coughing episode, and the respiratory effort associated with each cough. This provides a more accurate means of collecting cough frequency than relying on sound alone. There are also subjective measurements of cough impact on quality of life (e.g., the parent cough-specific QOL (PC-QOL) questionnaire described in “Development of a parent-proxy quality-of-life chronic cough-specific questionnaire: clinical impact vs psychometric evaluations,” Newcombe P A, Sheffield J K, Juniper E F, Marchant J M, Halsted R A, Masters I B, Chang A B, Chest. 2008 February; 133(2):386-95).

[0067] As another exemplary embodiment, the system could be used as a screening tool to identify sleep apnea severity and incidence in a clinical trial setting. In this embodiment, the contactless biomotion sensor is used to detect periods of no-breathing (apnea) and reduced amplitude breathing (hypopnea). FIG. 6 shows the estimated sleep apnea severity of the patients enrolled in a clinical trial, prior to therapy, as an example of how the system can be used.

[0068] The user skilled in the art will realize that the system can be used in a number of clinical trial settings where measurement of quality-of-life is important. As specific examples of such uses, we can consider:

[0069] Measurement of sleep quality of life in patients with atopic dermatitis (AD). Subjects with AD often have poor quality of life due to daytime itchiness combined with poor sleep quality due to subconscious scratching during sleep. In a clinical trial designed to assess the impact of an intervention such as a new drug or skin-cream, the system can be used to capture subjective and objective quality of life parameters as a final outcome measure. The outcome of the sleep quality-of-life index measurement can be a recommendation on whether to use a certain active medication, and the dosage of that medication.

[0070] Measurement of sleep quality in infants in response to feeding products. For example, lactose intolerance is known to affect quality-of-life in babies due to disrupted sleep, stomach pain, and crying episodes. Feeding products which aim to overcome lactose intolerance can be assessed by combination of objective sleep indices plus parent-reported crying episodes, to form an overall quality-of-life index.

[0071] As a further specific embodiment, sleep quality can be enhanced by providing a behavioral feedback program related to sleep quality of life. A person self-reporting insomnia can use the system as follows to enhance their sleep quality of life.

[0072] On a first visit with a physician, a person can self-report general dissatisfaction with their sleep quality of life. They can then choose to undertake a cognitive behavioral therapy program in the following steps.

[0073] Step 1: They undertake an induction session with a therapist or self-guided manual. In this induction step, the individual is introduced to information about basic physiological mechanisms of sleep such as normal physiological sleep patterns, sleep requirements, etc. This step ensures there are no incorrect perceptions of sleep (i.e. a person believing that 3 hours sleep a night is typical, or that you must sleep exactly 8 hrs per day for normal health).

[0074] Step 2: Bootzin stimulus control instructions. In this step, subject-specific information is established, and basic behavioral interventions are agreed. For example, firstly, the subject and therapies agree a target standard wake-up time (e.g., 7 AM). They then agree behavioral interventions such as getting out of bed after 20 minutes of extended awakening, and the need to avoid sleep-incompatible bedroom behavior (e.g., television, computer games, . . . ). They may agree to eliminate daytime naps.

[0075] Step 3: Establish initial target. Based on discussions above, the patient and therapist may then agree a sleep quality of life index (SQOLI) which will act as a target. As a specific example, the SQOLI may be based on achieving 85% sleep efficiency and a subjective “difficulty falling asleep” rating of <5 (on a 1-10. scale where 10 is very difficult and 1 is easy) The behavioral program will then consist of a week in which the patient tries to achieve the target based on going to bed 5 hours before the agreed wake-up time (e.g. at 2 AM in our example). The disclosure we have described above in FIGS. 1 to 5 provides the objective measurements of sleep efficiency and combines with the subjective user feedback to produce an SQOLI. At the end of the first week, the patient and therapist review the SQOLI measurements and determine the next step.

[0076] Step 4: Feedback loop based on Sleep Quality of Index. If the subject has achieved the desired SQOLI in the first week, then a new target is set. As a specific example, the subject will now go to bed 5.5 hours before the agreed wake up time, but will still try to achieve the same targets of 85% sleep efficiency and “difficulty falling asleep” metric <5. In subsequent weeks, the algorithm will be applied that the person can increase their sleep time by 30 minutes, provided they have met the targets in the previous week. This process can continue until a final desired steady state sleep quality of life index is reached (e.g., sleeping 7.5 hrs per night with a sleep efficiency of >85%).

[0077] The person skilled in the art will realize that a number of behavioral interventions have been developed and described in the literature for improving sleep quality. However a limitation of all these current approaches is that they do not have a reliable and easy means for providing the sleep quality of life metric, and it is this limitation which the current disclosure overcomes. Furthermore, the person skilled in the art will also realize that a number of pharmaceutical interventions are appropriate for improvement of sleep quality (e.g. prescription of Ambien®), and that the disclosure described here can support these medical interventions also.

STATEMENT OF INDUSTRIAL APPLICABILITY

[0078] The apparatus, system and method of this disclosure finds utility in contactless and minimum contact assessment of quality-of-life indices in clinical and consumer trials, and in interventions to improve the quality of life.