Transcutaneous electrical nerve stimulator with automatic detection of leg orientation and leg motion for enhanced sleep analysis, including enhanced transcutaneous electrical nerve stimulation (TENS) using the same
11259744 · 2022-03-01
Assignee
Inventors
Cpc classification
A61N1/0476
HUMAN NECESSITIES
A61B5/4809
HUMAN NECESSITIES
A61B5/11
HUMAN NECESSITIES
International classification
Abstract
Apparatus for providing transcutaneous electrical nerve stimulation (TENS) therapy to a user, the apparatus comprising: a housing; an application unit for providing mechanical coupling between the housing and the user's body; a stimulation unit for electrically stimulating at least one nerve of the user; a sensing unit for sensing the user's body movement and body orientation; and a reporting unit for providing the user with feedback based on the user's sensed body movement and body orientation.
Claims
1. Apparatus for monitoring the sleep patterns of a user, said apparatus comprising: a housing; an application unit for providing mechanical coupling between said housing and a leg of the user; a single sensing unit disposed within the housing for sensing (i) leg movement of the user, and (ii) body orientation and body roll of the user; and a processor disposed within the housing for using the leg movement of the user sensed by the single sensing unit and the body orientation and body roll of the user sensed by the single sensing unit to (i) determine when the user is in a sleep state, and (ii) analyze leg movement of the user while the user is in the sleep state.
2. Apparatus according to claim 1 further comprising a feedback unit for providing the user with a report in response to the analysis of the leg movement of the user.
3. Apparatus according to claim 1 wherein said leg movement includes periodic leg movements.
4. Apparatus according to claim 1 wherein analyzing said leg movement includes time and frequency patterns of said leg movement.
5. Apparatus according to claim 1 wherein electrical stimulation is provided in response to the analysis of the leg movement of the user.
6. Apparatus according to claim 1 wherein mechanical vibration is provided in response to the analysis of the leg movement of the user.
7. Apparatus according to claim 1 further comprising a feedback unit for providing the user with a report in response to the sensed body roll events.
8. Apparatus according to claim 1 wherein electrical stimulation is provided in response to the sensed body roll events.
9. Apparatus according to claim 1 wherein mechanical vibration is provided in response to the sensed body roll events.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
(1) These and other objects and features of the present invention will be more fully disclosed or rendered obvious by the following detailed description of the preferred embodiments of the invention, which is to be considered together with the accompanying drawings wherein like numbers refer to like parts, and further wherein:
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DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
The Novel TENS Device in General
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(13) TENS device 100 is shown in greater detail in
(14) Still looking now at
(15) The preferred embodiment of the present invention is designed to be worn on the upper calf 140 of the user as shown in
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(17) Further details regarding the construction and use of the foregoing aspects of TENS device 100 are disclosed in (i) U.S. Pat. No. 8,948,876, issued Feb. 3, 2015 to NeuroMetrix, Inc. and Shai N. Gozani et al. for APPARATUS AND METHOD FOR RELIEVING PAIN USING TRANSCUTANEOUS ELECTRICAL NERVE STIMULATION, which patent is hereby incorporated herein by reference, and (ii) pending prior U.S. patent application Ser. No. 14/230,648, filed Mar. 31, 2014 by Shai N. Gozani et al. for DETECTING CUTANEOUS “ELECTRODE PEELING” USING ELECTRODE-SKIN IMPEDANCE, published as U.S. Patent Application Publication No. US 2014/0296934 A1 on Oct. 2, 2014, which patent application is hereby incorporated herein by reference.
The User State (i.e., Leg Orientation and Leg Motion) Detector
(18) In accordance with the present invention, TENS device 100 further comprises (e.g., within compartment 102) user state (i.e., leg orientation and leg motion) detector 500 for (i) determining the sleep-wake state of the user, (ii) analyzing the sleep of the user, and/or (iii) providing enhanced transcutaneous electrical nerve stimulation (TENS) using the same. To this end, and looking now at
(19) When the TENS device is secured in position on the user's upper calf, the position and orientation of accelerometer 152 (
(20) Data from accelerometer 152 are analyzed in real time by processor 515 of user state (i.e., leg orientation and leg motion) detector 500 to determine the orientation and motion of the lower limb (i.e., upper calf 140) of the user. The orientation, motion, and activity level of the lower limb (i.e., upper calf 140) of the user, determined by analyzing the data from accelerometer 152, are used to determine the sleep-wake state and sleep patterns of the user. Based on the sleep-wake state and sleep patterns, TENS device 100 can modify its stimulation pattern (such as the stimulation intensity level and the onset of the stimulation) via controller 520, or provide the user with additional feedback (such as mechanical vibration if the duration of the sleep-on-back state exceeds a threshold).
(21) The leg orientation and leg motion components measured by the user state (i.e., leg orientation and leg motion) detector 500 of the present invention may individually or collectively contribute to the determination of the sleep-wake state of the user. In one preferred form of the invention, processor 515 of TENS device 100 measures the calf orientation of the user, which is highly correlated with the body orientation of the user. More particularly, upright body orientation is generally a reliable indicator that the user is in a wake state, while recumbent orientation suggests a resting state (e.g., such as occurs during sleep). Regular and robust body movement is more likely the result of user activities during the daytime (i.e., during wake state), while quiet or low-level spontaneous movements are more likely during nighttime (i.e., during sleep state). Interactions of body orientation and movement level can also be useful in identifying the sleep-wake state of the user (i.e., thereby enhancing a sleep-wake state classification). Specifically, recumbent body orientation and a low-level of physical activity is generally a good indicator that the user is asleep.
(22) In addition, real-time clock 505 of user state (i.e., leg orientation and leg motion) detector 500 allows assigning a nontrivial a priori probability of the sleep-wake state at any given time of the day in order to further refine the sleep-wake state classification results obtained by the aforementioned analysis of leg orientation and leg motion data (i.e., a user is more likely to be asleep at 3:00 am and less likely to be asleep at 4:00 pm). In a preferred embodiment of the present invention, to reflect that the a priori probability that the sleep state is low at a specific daytime window, the threshold value for classifying user body orientation as recumbent can be made more stringent.
(23) In another embodiment of the present invention, output from ambient light sensor 510 is used to improve sleep-wake classification results. The ambient light sensor 510 can be used to determine if the user is in an environment which has an illuminated or non-illuminated ambience, to reflect the a priori probability that a user is more likely to be sleeping in a dark setting than in a brightly lit setting. Accordingly, the threshold values for classifying user body position and motion level can be adjusted to reflect the a priori probability of sleep.
On-Skin Detector
(24) In one preferred form of the invention, TENS device 100 may comprise an on-skin detector to confirm that TENS device 100 is firmly seated on the skin of the user.
(25) More particularly, the orientation and motion measures from accelerometer 152 in TENS device 100 only become coupled with the orientation and motion of a user when the TENS device is worn by the user. In a preferred embodiment, an on-skin detector 521 is provided to determine whether and when TENS device 100 is securely placed on the user's upper calf. In the preferred embodiment, and looking now at
(26) On-skin detector 521 is preferably employed in two ways.
(27) First, if on-skin detector 521 indicates that electrode array 120 of TENS device 100 has become partially or fully detached from the skin of the user, TENS device 100 can stop applying TENS therapy to the user.
(28) Second, if on-skin detector 521 indicates that electrode array 120 of TENS device 100 has become partially or fully detached from the skin of the user, processor 515 of TENS device 100 will recognize that the data from accelerometer 152 may not reliably reflect user leg orientation and leg motion, and user state (i.e., leg orientation and leg motion) detector 500 can take appropriate action (e.g., alert the user). In this respect it should be appreciated that, when the on-skin detector 521 indicates that TENS device 100 is on the skin of the user, and accelerometer 152 is closely coupled to the lower limb of the user, the data from accelerometer 152 may be representative of user leg orientation and user leg motion. However, when the on-skin detector 521 indicates that TENS device 100 is not on the skin of the user, accelerometer 152 is not closely coupled to the lower limb of the user, and the data from accelerometer 152 will not be representative of user leg orientation and user leg motion.
Accelerometer Data Processing
(29) In one preferred form of the invention, user state (i.e., leg orientation and leg motion) detector 500 comprises a processor 515 for taking the accelerometer data from accelerometer 152 and calculating user activity (e.g., body orientation, body movement and activity levels).
(30) More particularly, in one preferred form of the invention, processor 515 uses the accelerometer data from accelerometer 152 to measure the user's leg orientation, which is highly correlated with body orientation and therefore indicative of the user's recumbent state (and thereby the user's sleep-wake state); and processor 515 uses the accelerometer data from accelerometer 152 to measure the user's leg motion, which is also indicative of the user's sleep-wake state and leg motion activity levels; and processor 515 uses the determinations of user leg orientation and user leg motion to enhance sleep quantification.
(31) More particularly, processor 515 uses the accelerometer data from accelerometer 152 to measure two distinct aspects of the user's leg orientation: leg “elevation” (or the angle of the lower leg relative to the horizontal plane), and leg “rotation” (or the angle of rotation of the lower leg about its own axis).
(32) And processor 515 uses the accelerometer data from accelerometer 152 to measure two distinct aspects of leg motion: “net activity” (which is the magnitude of movement-related acceleration averaged within one-minute windows), and “leg movements” (or brief events that are known to occur in sleep but are not evident in net activity). Some leg movements accompanied by a large leg rotation may be further classified as “body roll events” (such as occur when rolling over in bed).
(33) In a preferred embodiment of the present invention, processor 515 for calculating user activity (e.g., body orientation, body movement and activity levels) is constructed and configured to operate as follows. Raw accelerometer data produced at 400 Hz are decimated to 50 Hz. Following that, the time scale of an “instant” is defined to be equal to 0.1 sec. The 50 Hz data on each axis (x, y, z) are separately averaged over each instant, to provide a low-noise data stream at 10 Hz, denoted by A.sub.x(t), A.sub.y(t), and A.sub.z(t).
(34) The accelerometer data A.sub.x(t), A.sub.y(t), and A.sub.z(t) are used to form features which are averages of A.sub.x(t), A.sub.y(t), and A.sub.z(t) over a longer time window (e.g., a one minute window) to capture the steady-state projection of earth gravity along each axis (x, y, z). These features are used for detecting leg orientation (i.e., leg elevation and leg rotation).
(35) Additionally, the accelerometer data A.sub.x(t), A.sub.y(t), and A.sub.z(t) are high-pass filtered to remove the static gravity component in order to isolate acceleration components caused by leg movement. The high-pass filter has −3 dB point at 0.5 Hz. High-pass filtered accelerometer data are denoted Ã.sub.x(t), Ã.sub.y(t), and Ã.sub.z(t).
Leg Elevation Detection
(36) In one preferred form of the invention, user state (i.e., leg orientation and leg motion) detector 500 is configured to detect leg elevation.
(37) More particularly, in order to determine the “body orientation state” for the purpose of sleep monitoring, the present invention uses the leg elevation, which is computed by processor 515 of user state (i.e., leg orientation and leg motion) detector 500, based on measurement data from accelerometer 152 when TENS device 100 is placed on the user's upper calf 140 (
(38) A stationary upright user, or one sitting with feet resting on the ground, will have an upright calf elevation. Consequently, the y-axis acceleration of accelerometer 152 will have a value of about −1 g due to Earth gravity 154 (
(39) Looking now at
(40) In general, the acceleration measured along the y-axis will include not only the projection of gravity onto that axis, but also a contribution from motion:
A.sub.y(t)=±sin |θ(t)|+m(t)[in unit of g]
where t is time, and m(t) is the contribution due to leg motion. The specific ± sign depends upon the TENS device placement on upper calf 140 and is fixed for each placement. The motion component m(t) is considered “noise” in the context of determining leg elevation, and will have zero mean over a sufficiently large window.
(41) In a preferred embodiment, a leg elevation algorithm, taking into account user body movement, is implemented by processor 515 of user state (i.e., leg orientation and leg motion) detector 500 in the following manner.
(42) Step 1. Set a target angle threshold θ.sub.0 (this is the “Threshold1” shown at step 910 in
(43) Step 2. Define non-overlapping windows of length N, called “epochs”. The time at the end of each epoch is denoted T. In a preferred embodiment, the accelerometer data (in units of g, standard earth gravity) are segmented into epochs, i.e., one-minute windows. With an accelerometer data rate of 10 Hz, the epoch length is N=600. The mean A.sub.y,T and the standard error of the mean SE.sub.Y,T are calculated based on samples in each epoch.
(44) Step 3. Let θ.sub.T=sin.sup.−1A.sub.y,T. Values of θ.sub.T≈θ.sub.0 can lead to erratic switching of the leg elevation state. In order to reduce this, define a hysteresis band θ.sub.0±θ.sub.H. In the preferred embodiment, the hysteresis parameter θ.sub.H is set to 2.50, but other values are possible (but should be small compared to θ.sub.0). In the preferred embodiment, rather than computing sin.sup.−1 for every epoch, the angular thresholds are instead converted to acceleration units, i.e., by computing two thresholds A.sub.±=sin(θ.sub.0±θ.sub.H), against which A.sub.y,T will be compared.
(45) Step 4. The ability of the hysteresis band to prevent erratic switching of the leg elevation state depends upon the amount of noise in the data, characterized by SE.sub.Y,T, which is the standard error of the mean A.sub.y,T. In order to account for the noise level in the data, processor 515 of user state (i.e., leg orientation and leg motion) detector 500, processor 515 compares the acceleration data A.sub.y,T to the thresholds A.sub.±. However, instead of comparing the mean A.sub.y,T per se to the thresholds A.sub.±, processor 515 compares the “confidence interval” A.sub.y,T±ηSE.sub.Y,T to the thresholds A.sub.±. More specifically, for each epoch, if the prior elevation state was recumbent, in order to classify the next state as upright, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 requires [|A.sub.y,T|−ηSE.sub.Y,T]>A.sub.+. If the prior elevation state was upright, in order to classify the next state as recumbent, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 requires [|A.sub.y,T|+ηSE.sub.Y,T]<A.sub.−. In a preferred embodiment η=3, but other values are possible.
Instantaneous Activity
(46) In one preferred form of the invention, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 may be configured to detect instantaneous activity.
(47) More particularly, when TENS device 100 is worn on the user's upper calf 140, the user's activity will be captured by accelerometer 152 of the TENS device. Each axis (x, y, z) of accelerometer 152 measures the projection of the acceleration vector along that axis. As described above, the measured acceleration includes the static effect of earth gravity, as well as contributions from leg movement. In order to isolate the contributions from leg movement, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 high-pass filters the instant data vector A(t)=[A.sub.x(t),A.sub.y(t),A.sub.z(t)] before further processing.
(48) Although the acceleration component for each individual axis of the accelerometer contains unique and useful information for body movement analysis, the vector magnitude of acceleration, called the “instantaneous acceleration”, denoted Ã.sub.I(t) and defined in equation below, is commonly used to quantify the overall motion-related activity:
Ã.sub.I(t)=√{square root over (Ã.sub.X(t).sup.2+Ã.sub.Y(t).sup.2+Ã.sub.Z(t).sup.2)}
In a preferred embodiment of the present invention, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 uses this instantaneous acceleration Ã.sub.I(t) for the actigraphy calculations. However, calculations based on other combinations of acceleration axes may also be used. For example, rather than combining all three axes equally as done with Ã.sub.I(t) as defined above, only some axes may be used, or certain axes may be contrasted through subtraction.
Leg Movement Detector
(49) In one preferred form of the invention, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 may be configured to detect leg movement.
(50) More particularly, the instantaneous acceleration Ã.sub.I(t) is a time series comprised of brief events, such as leg movements known to occur during normal and abnormal sleep, and sustained activity, such as occurs during walking, running, or climbing stairs. In a preferred embodiment, leg movements (LM) are computed in a manner that is consistent with the detection of periodic leg movements (PLM) defined in the clinical literature (Bonnet et al, 1993; Zucconi et al, 2006), however, other approaches to detecting brief leg movements are possible and are considered to be within the scope of the present invention.
(51) In the preferred embodiment, a leg movement (LM) detection algorithm is implemented by processor 515 of user state (i.e., leg orientation and leg motion) detector 500 in the following manner.
(52) Step 1. Define two thresholds (these are the “Threshold2” and “Threshold3” shown at steps 914 and 918, respectively, in
(53) Step 2. Define an instantaneous activity state (IAS) and initialize the IAS to False.
(54) Step 3. Compute instantaneous acceleration Ã.sub.I(t) for each time instant.
(55) Step 3. Update the IAS for each time instant as follows. If IAS=False and Ã.sub.I(t)>0.03 g, then set IAS=True. If IAS=True and Ã.sub.I(t)<0.02 g, then set IAS=False. Two thresholds used in this way implement hysteresis in a simple way to prevent rapid switching in the IAS.
(56) Step 4. When IAS becomes True, a leg movement (LM) period begins. When IAS becomes false and remains false for more than 0.5 second, the LM period ends. Thus a contiguous time interval in which IAS=True, and surrounded by intervals in which IAS=False, comprises a leg movement (LM) period. However, if contiguous intervals for which IAS is True are separated by less than 0.5 second, the brief interval for which IAS was False is ignored.
(57) The top panel (810) in
Body Roll Detector
(58) In one preferred form of the invention, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 is configured to function as a body roll detector.
(59) More particularly, when the TENS device 100 (
β=180−α−φ
Because the angle α is fixed, the leg rotation angle β can be derived from the angle φ as measured by the accelerometer 152.
(60) Some brief increases in activity that are classified as leg movement (LM) are associated with large changes in the roll angle φ measured by the TENS device 100. Rolls of sufficient magnitude are unlikely to involve only the leg, but rather are likely to indicate that the entire body is rolling over while in bed, e.g., from the left side to the right side, or from the back to the left side or the right side. Some leg movements (LMs) may therefore be classified as “body roll events”.
(61) In one preferred embodiment, a body roll detection algorithm is implemented by processor 515 in user state (i.e., leg orientation and leg motion) detector 500, using only the angle change Δφ, in the following manner:
(62) Step 1. For each LM period detected, select the raw acceleration vector A(t) in short windows before and after the leg movement. In a present invention, this window is an instant (0.1 seconds).
(63) Step 2. Before and after each LM period, take the instant values of A(t) (not high-pass filtered) on each axis separately so as to obtain A.sub.x(t), A.sub.y(t), and A.sub.z(t).
(64) Step 3. Using these values before and after the LM, compute the rotation angle φ(t)=a tan 2{A.sub.x(t), A.sub.z(t)}. The inverse tangent function a tan 2 returns an angle in the range −180°<φ(t)≤180°, i.e., a result in all four possible quadrants.
(65) Step 4. Compute the change in rotational angle Δφ=φ.sub.after−φ.sub.before. In order to facilitate comparison with a threshold (this is the “Threshold4” shown at step 924 in
(66) Step 5. Compare the absolute value |Δφ| with a threshold value. In the present invention, this threshold value is 50°, but other values may be used. If |Δφ|>50°, then classify the LM event as a “body roll event”.
(67) The middle panel (820) in
(68) The bottom panel (830) of
(69) These body rolls may be reported directly to the user to inform them about their sleep patterns. In addition, because body roll events may be brief, the associated increase in activity may not be evident in the epoch average of activity, and therefore may not cause that epoch to be classified as awake. Although rolling over in bed may not indicate an awake state, it does indicate momentarily restless sleep. This novel approach for detecting body rolls by evaluating changes in roll angles associated with brief leg movement (LM) permits the differentiation of leg movement associated with no body rolls from leg movement associated with body rolls, and thus provides a finer description of sleep patterns that are helpful in clinical diagnosis.
(70) In another preferred embodiment, rather than using single instants of A(t) before and after the LM to compute the angles φ, the mean or median values of A(t) over several instants before and after the LM are used to improve robustness to noise.
(71) In another preferred embodiment, a body roll detection algorithm is implemented by processor 515 of user state (i.e., leg orientation and leg motion) detector 500 using the angle change Δβ in the following manner. Consider a person lying on their back, with the TENS device placed on their right leg. Recalling that, with the TENS device placed on either leg, β=0 when the toes are pointed vertically upward, and β increases with counterclockwise (CCW) rotation, therefore the most likely range of leg rotational positions is −80°≤β≤0°. Any change in angle Δβ that remains within that range may not likely be associated with a body roll. In contrast, a change in angle Δβ from inside that range to outside that range is most likely associated with a body roll. In this way, using the change in angle Δβ, the threshold for detecting a body roll may be adjusted depending upon the leg on which the device is placed. That is to say, in addition to the magnitude of the change Δβ, the value of the leg rotation angle β before and after the leg movement (LM), and the sign of the angle change Δβ across the leg movement (LM), may be used to improve performance of the body roll detector.
Static Body Rotational Position Detector
(72) In one preferred form of the invention, processor 515 of user state (i.e., leg orientation and leg motion) detector 500 may be configured to function as a static body rotational position detector.
(73) More particularly, users with sleep apnea are recommended not to sleep on their back.
(74) Because of the limited rotational range of motion of the human hip, leg rotational position is highly correlated with body position, e.g., when sleeping on one's back, the toes of either foot are pointed upward above the horizontal plane to varying degrees, not likely exactly on the horizontal plane, and never below the horizontal plane. This observation, together with the placement of the novel TENS device on the upper calf of the user, allows an innovative addition to sleep analysis.
(75) The time scale of an “epoch” equal to one minute, and the epoch-averaged non-high-pass filtered acceleration values Ā.sub.X,T(t), Ā.sub.Y,T(t), and Ā.sub.Z,T(t) were introduced above in the section entitled “Leg Elevation Detection”. Because it is sufficient to report the time spent sleeping on the back at the resolution of one minute, these epoch-averaged acceleration values may be advantageously used in the following manner to detect static body rotational position.
(76) Consistent with the roll detector definition of the rotational position angle φ, let φ.sub.T=a tan 2{Ā.sub.X,T(t), Ā.sub.Z,T(t)} as before, where Ā.sub.X,T(t) and Ā.sub.Z,T(t) are raw (i.e., not high-pass filtered) accelerations averaged over an epoch T. Let β.sub.T=the angle of the toes relative to the vertical. The relation between φ.sub.T and β.sub.T depends upon the rotational placement of the TENS device on the upper calf of the user, denoted α. Because the electrode gel 444 is sticky and the strap 110 is supportive, the TENS device does not move on the user's leg once it is placed onto the upper calf 140, therefore the angle α is constant as long as the TENS device is on the leg of the user.
(77) Looking now at
(78) In a preferred embodiment, the following simple procedure is used by processor 515 of user state (i.e., leg orientation and leg motion) detector 500 to determine whether the user is on-back through an estimation of the angle β.
(79) Step 1. The user places the TENS device on the lower leg of the user and fastens the strap 110 snugly around their upper calf 140, lies recumbent with the leg nearly horizontal, points their toes vertically upward, and remains still.
(80) Step 2. The user indicates to the TENS device that the aforementioned conditions have been met. This indication may take the form of a series of button presses (e.g., with button 106), a series of taps on compartment 102 detected by the accelerometer 152, or an indication on a smartphone 860 in communication with the TENS device 100.
(81) Step 3: With the toes pointed upright, β≈0, therefore it is trivial to estimate {circumflex over (α)}=180−{circumflex over (φ)} where {circumflex over (φ)} is estimated from accelerometer data acquired during the toe-up period. In order to facilitate calculations, put this difference in the range −180°<{circumflex over (α)}≤180°, i.e., if {circumflex over (α)}>180° then subtract 360°, but if {circumflex over (α)}≤−180° then add 360°.
(82) Step 4: In every epoch ending at time T, use this value of {circumflex over (α)} to compute β.sub.T=180−{circumflex over (α)}−φ.sub.T. In order to facilitate comparisons with a threshold, put this difference in the range −180°<β.sub.T≤180°, i.e., if β.sub.T>180° then subtract 360°, but if β.sub.T≤−180° then add 360°.
(83) Step 5: Define a range of values for β.sub.T that correspond to the user lying or sleeping on their back. In a preferred embodiment, classify every epoch for which −80°<β.sub.T<80° as “on-back”. This range is symmetrical so the algorithm works for placement on either leg. Avoiding ±90° by 10° excludes the values likely to be encountered when a user lies or sleeps on their side. In another preferred embodiment, the thresholds (which would reside at step 930 in
(84) Step 6: If the user with sleep apnea selects this option for TENS device 100, then when the user is determined to be asleep, i.e., recumbent with low activity, the TENS device notifies the user if they are on their back for more than some set amount of time, e.g., a few minutes. This indication can be in the form of a vibration of the TENS device itself, or an alarm on their smartphone 860, for example.
(85) Step 7: After determining the span(s) of minutes in which the user was likely to be asleep, i.e., recumbent with low activity, determine the fraction of minutes in which the user was determined to be on their back. Report this percentage to this user, e.g., with smartphone 860.
Exemplary Operation
(86) In one preferred form of the invention, TENS device 100, including its user state (i.e., leg orientation and leg motion) detector 500, its processor 515 and its controller 520, are programmed to operate in the manner shown in the flowchart of
(87) More particularly, when TENS device 100 is secured to the upper calf 140 of the user and turned on, user state (i.e., leg orientation and leg motion) detector 500 collects data from accelerometer 152, real-time clock 505 and ambient light detector 510, as shown at step 902. In addition, on-skin detector 521 confirms that electrode array 120 of TENS device 100 is in contact with the user's skin, as shown at step 904 (and hence confirms that TENS device 100 is secured to the upper calf 140 of the user).
(88) Processor 515 analyzes data from accelerometer 152, real-time clock 505 and ambient light detector 510, as shown at step 906.
(89) Processor 515 determines the user's leg elevation orientation, as shown at step 908, and determines if the user is in bed by comparing elevation angle with a threshold (i.e., “Threshold4”), as shown at step 910.
(90) If processor 515 determines that the user is in bed, processor 515 determines the user's leg activity, as shown at step 912.
(91) The user's leg activity is compared against a threshold (i.e., “Threshold1”), as shown at step 914, and, if the user's leg activity is below that threshold, processor 515 determines that the user is in a restful sleep, as shown at step 916.
(92) Processor 515 also compares the user's leg activity (determined at step 912) against another threshold (i.e., “Threshold2”), as shown at step 918, and, if the user's leg activity is above that threshold, processor 515 determines that the user has excessive leg movement, as shown at step 920.
(93) In addition to the foregoing, processor 515 also determines the user's leg rotation orientation, as shown at step 922, and compares the change in the angle of the user's leg rotation against another threshold (i.e., “Threshold3”), as shown at step 924, and, if the change in the angle of the user's leg rotation is above that threshold, and if the user's leg movement exceeds a threshold (i.e., “Threshold2”) as shown at step 918, processor 515 determines that a body roll event has occurred, as shown at step 926.
(94) Also, processor 515 looks at the user's leg rotation orientation, as determined at step 922, the accelerometer data analysis, as determined at step 906 and the user's user limb and toe-up indication, as determined at step 928, and determines the user's body position classification, as shown at step 930. Processor 515 then characterizes the user's position as “on back”, “on side (left/right)” or “on stomach”, as shown at step 932.
(95) The information derived at steps 916, 920, 926 and 932 is then utilized by processor 515 to analyze the user's sleep session, as shown at step 934. The results of this sleep analysis (as determined at step 934) may then be displayed (as shown at step 936), used to provide feedback to the user or the user's caregiver (as shown at step 938) and/or used to direct controller 520 (as shown at step 940) to modulate the stimulation current provided by TENS device 100.
Modifications of the Preferred Embodiments
(96) It will be appreciated that the present invention provides a transcutaneous electrical nerve stimulator with automatic monitoring of leg activities and leg orientations. Leg orientations include leg elevation and leg rotation state, and changes in leg elevation and leg rotation states. The TENS stimulator may be pre-programmed to modify its operations in response to the detected user leg activities and leg positions during bed time. In addition, leg orientation and leg activities are used to assess sleep quality and sleep position, all are important aspects to improve sleep and health. Leg activity patterns can also be used to diagnose sleep disorders such as periodic leg movement and the TENS stimulator can be used to alleviate excessive leg movement activities that are disruptive to sleep.
(97) The present invention can also be realized without the nerve stimulation functionality. Body movement and position can be monitored and quantified using the present invention without the need of nerve stimulation. The monitoring apparatus (device) can also be placed in other body positions like upper arm of either limb.
(98) Furthermore, it should be understood that many additional changes in the details, materials, steps and arrangements of parts, which have been herein described and illustrated in order to explain the nature of the present invention, may be made by those skilled in the art while still remaining within the principles and scopes of the invention.