DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant's election with traverse of Group 1 in the reply filed on 05/05/2026 is acknowledged. The traversal is on the grounds that Group I and Group II have unity of invention since the method of Group II is closely linked to the apparatus of Group II and a proper search of the claims in Group I would necessarily include the classes and subclasses of Group II.
Examiner has considered Applicant’s arguments, but they have not been found persuasive because the shared technical features of processing physiological signals to classify signals as normal/abnormal and generate a signal indicating an imminent loss of balance is not a special technical feature that makes a contribution over the prior art. Examiner maintains that despite the shared technical features of Groups I and II, a comprehensive search of the limitations of one group would not contain all of the relevant classes and subclasses of the other group.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, 6-8, 10-15, and 19 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2, 3, 6-8, 10-15, and 19, the phrase "preferably" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 is directed to the abstract idea/mathematical concept of detecting an imminent loss of balance for a subject by classifying one or more physiological signals in the form of an EEG and/or EMG signal, which is directed to organizing information and manipulating information through mathematical correlations.
Step 1
Claim 1 recites a method.
Step 2A, Prong 1
Claim 1 recites the limitations of:
- analysis and processing of said plurality of electromyographic signals (SEMG) in order to extract at least one (MAP(k)) muscle activity pattern, MAP, for the detected muscle activity, and generate at least one indicator (MAScore(k); MA(k)) of normality/abnormality of the detected muscle activity pattern;
- analysis and processing of said plurality of brain signals (SEEG) in order to generate a plurality of cortical response indicators (IEGg; LF(k)) for the cortical response of the subject upon occurrence of said detected muscle activity;
- classification, wherein at least one indicator (MA(k)) of MAP normality/abnormality and one or more of said cortical response indicators are correlated to generate a signal (Aout) indicating an imminent loss of balance; wherein the cortical response indicators for the cortical response of the subject used in the classification step include at least one indicator of normality/abnormality of the cortical response generalized over one or more cortical macro-areas of the subject upon occurrence of said muscular activity, and an indicator of lateralization of the cortical response, which indicates a normality/abnormality of the involvement of the left and right cortical sides in the cortical response; and wherein, in the classification step, a signal (Aout) indicating an imminent loss of balance is generated if at least one anomaly in a generalized cortical response over one or more cortical macro-areas, a presence of a non-lateralized anomalous cortical response and a simultaneous abnormality of the muscle activity pattern are detected.
These steps, given their broadest reasonable interpretation, are directed to an abstract idea/mathematical concept wherein information is organized and manipulated through mathematical correlations. The EEG and EMG signals are organized and manipulated to produce indicators of normality/abnormality which are then used to classify if the subject is experiencing an imminent loss of balance. See MPEP 2106.04(a)(2)(I)(A).
Step 2A, Prong 2
Claim 1 does not include any additional elements that integrate the abstract idea into a practical application.
Claim 1 includes the additional elements of:
- reception of a plurality of electromyographic signals (SEMG) representative of a detected muscle activity of a plurality of selected muscles of the subject;
- reception of a plurality of brain signals (SEEG), acquired by means of electroencephalogram and representative of a cortical activity of the subject during said muscle activity; and
- generating a signal (Aout) indicating an imminent loss of balance based on a correlation of at least one indicator of MAP normality/abnormality and one or more cortical response indicators.
The additional elements of reception of a plurality of electromyographic signals (SEMG) and reception of a plurality of brain signals (SEEG) are identified as extra-solution activity of mere data gathering, in the form of performing clinical tests, in this case recording/receiving a plurality of electromyographic and brain signals and analyzing/processing said signals to generate indicators, to obtain input for an equation, wherein the classification of the indicators to detect an imminent loss of balance is the equation. See MPEP 2106.05(g), In re Grams, 888 F.2d 835.
The additional element of generating a signal Aout indicating an imminent loss of balance is identified as extra-solution activity of necessary data outputting. See MPEP 2106.05(g).
Step 2B
Claim 1 does not include any additional elements that amount to significantly more than the abstract idea.
Claim 1 includes the additional elements of:
- reception of a plurality of electromyographic signals (SEMG) representative of a detected muscle activity of a plurality of selected muscles of the subject;
- reception of a plurality of brain signals (SEEG), acquired by means of electroencephalogram and representative of a cortical activity of the subject during said muscle activity; and
- generating a signal (Aout) indicating an imminent loss of balance based on a correlation of at least one indicator of MAP normality/abnormality and one or more cortical response indicators; which have been identified as extra-solution activity as discussed above with respect to Step 2A, Prong 2.
Additionally, the additional elements of receiving a plurality of electromyographic signals/brain signals and generating an output signal can be held to be well-understood, routine, and conventional in the art; and they are recited with a high level of generality which does not amount to significantly more than the abstract idea itself.
Claims 2, 3, 12, and 13 further limit the extra-solution activity of data gathering.
Claims 4-11 and 14-20 further define the abstract idea/mathematical concept of classification.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mezzina et al (G. Mezzina, F. Aprigliano, S. Micera, V. Monaco and D. D. Venuto, "EEG/EMG based Architecture for the Early Detection of Slip-induced Lack of Balance," 2019 IEEE 8th International Workshop on Advances in Sensors and Interfaces (IWASI), Otranto, Italy, 2019, pp. 9-14, doi: 10.1109/IWASI.2019.8791252), accessed via https://www.iris.sssup.it/handle/11382/532535.
Regarding claim 1, Mezzina teaches a method of processing physiological signals (SEMG;SEEG) acquired from a subject, for detecting an imminent loss of balance of the subject and generating a signal indicating the imminent loss of balance (see Abstract; a novel pre-impact fall detection, PIFD, strategy based on EMG and EEG signals), comprising the steps of:
- reception of a plurality of electromyographic signals (SEMG) representative of a detected muscle activity of a plurality of selected muscles of the subject (see [Page 10, II. The PIFD System]; EMG signals are properly treated to identify the contraction onsets of each monitored muscle);
- reception of a plurality of brain signals (SEEG), acquired by means of electroencephalogram and representative of a cortical activity of the subject during said muscle activity (see [Page 10, II. The PIFD System]; the EEG signals allow the system to investigate the cortical activity related to subject steps);
- analysis and processing of said plurality of electromyographic signals (SEMG) in order to extract at least one (MAP(k)) muscle activity pattern, MAP, for the detected muscle activity (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; for each trigger, or muscle, after analysis, results in a binary vector, which considers the states of all the muscles, called the Muscular Activity Pattern), and
generate at least one indicator (MAScore(k);MA(k)) of normality/abnormality of the detected muscle activity pattern (see [Pages 12-13, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment, Fig. 5b]; scores RL and LL; in the presence of a perturbation, due to the parallel activation of normally nonconcurrent muscles, it was an expected an abnormal behavior),
- analysis and processing of said plurality of brain signals (SEEG) in order to generate a plurality of cortical response indicators (IEGg; LF(k)) for the cortical response of the subject upon occurrence of said detected muscle activity (see [Page 13, E. The EEG Computation Branch]; detection of the lateral Gastrocnemius rising edge enables the EEG processing branch to extract and process EEG signals surrounding the time of the trigger);
- classification, wherein at least one indicator (MA(k)) of MAP normality/abnormality and one or more of said cortical response indicators are correlated to generate a signal (Aout) indicating an imminent loss of balance (see [Page 13, F. Logical Conditions Network]; 2. once the initial thresholds are extracted the system waits for a new MT contraction and if the MT rising edge is detected the muscular score is compare with the initial threshold);
wherein the cortical response indicators for the cortical response of the subject used in the classification step include at least one indicator of normality/abnormality of the cortical response generalized over one or more cortical macro-areas of the subject upon occurrence of said muscular activity (see [Page 13, F. Logical Conditions Network]; jointly analyzes the muscular activity and the cortical involvement of a subject that actively reacts to recover the perturbed balance, the system statistically derives thresholds and a network of logical conditions to detect the loss of balance induced by the slippage), and
an indicator of lateralization of the cortical response, which indicates a normality/abnormality of the involvement of the left and right cortical sides in the cortical response (see [Page 12, Fig, 5]; demonstrating an indication of lateralization and an abnormality of involvement in one side over the other, wherein in Fig. 5 at the time of perturbation the left side has an abnormal response, [Page 13, E. The EEG Computation Branch, 2) Cortical Responses Analysis]; a linear model of the measured data is extracted for each band of interest, wherein the slope of the model is used to describe the cortical responsiveness, where a sharp increment of the slope indicated a greater involvement of the cortical area); and
wherein, in the classification step, a signal (Aout) indicating an imminent loss of balance is generated if at least one anomaly in a generalized cortical response over one or more cortical macro-areas, a presence of a non-lateralized anomalous cortical response and a simultaneous abnormality of the muscle activity pattern are detected (see [Page 13, F. Logical Conditions Network]; 2. once the initial thresholds are extracted the system waits for a new MT contraction and if the MT rising edge is detected the muscular score is compare with the initial threshold; the architecture will enable a potential feedback procedure if there is a parallel presence of a muscular alert and at least the 51% of the available EEG warnings).
Regarding claim 2, Mezzina teaches the method according to Claim 1, wherein each electromyographic signal received is digitized by means of a threshold system in order to obtain a corresponding binary signal (OOMx;MT) of muscle activation for a respective selected muscle (see [Page 12, D. The EMG Computation Branch, 1) Trigger Extraction]; muscle trigger extraction comprises a dynamic threshold approach in which each EMG signal was converted into a binary signal, it is high when the muscle is contracted and low otherwise, the method consists of comparing the average signal power over a time span M, PM, to the average signal power over a shorter time span N, PN, and if PN is larger than PM, the trigger goes high),
wherein preferably the threshold system is a moving threshold system configured to adapt to changes in muscle tone (It can be appreciated that this method of sample-by-sample dynamic threshold updating can account for and adapt to changes in muscle tone).
Regarding claim 3, Mezzina teaches the method according to Claim 1, wherein the plurality of electromyographic signals (SEMG) includes signals representative of a muscle activity detected, bilaterally, from one or more, preferably all, of the following muscles of the subject: Anterior Tibial (AT), Lateral Gastrocnemius (LG), Vastus Medialis (VM), Rectus Femoris (RF) and Biceps Femoris (BF) (see [Page 11, B. Experimental Setup]; the 10 surface EMG channels were monitored from bilateral muscle groups including the anterior tibialis, lateral gastrocnemius, vastus medialis, rectus femoris, and biceps femoris).
Regarding claim 4, Mezzina teaches the method according to Claim 1, wherein the MAP pattern is extracted taking into account the contraction state of the selected muscles upon contraction of a reference muscle (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; the system studied the occurrence of a specific logic state for each single trigger in correspondence of the MT onset, this process leads to the definition of a vector F1R ∊ ℝTr, with "1" the selected state, "R" the reference to the considered MT).
Regarding claim 5, Mezzina teaches the method according to Claim 1, comprising correlating, in particular comparing, an extracted muscle activity pattern MAP with a standard muscle behaviour model, to generate an indicator of MAP normality/abnormality (see [Fig. 5a]; which sketches the processing algorithm for the statistical coherence degree assessment of the i-th step related MAP, [Fig. 4]; wherein the table shows the weights related to F1R and F0R with relation to the reference muscle behavior model).
Regarding claim 6, Mezzina teaches the method according to Claim 5, comprising quantifying with a scoring method a degree of similarity between the detected muscle activity pattern (MAP(k)) and the standard muscle behaviour model in order to obtain a score (MAScore) of normality/abnormality of the detected muscle activity pattern, wherein the score (MAScore) is preferably a scalar value (see [Pages 12-13, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment, Fig. 5b]; scores RL and LL are calculated using a progressive summation according to equation 4, which results in the scores as plotted in Fig. 5b having a scalar value between 0 and 1).
Regarding claim 7, Mezzina teaches the method according to Claim 1 wherein, for the classification step, at least one binary indicator (MA(k)) of MAP normality/abnormality is generated, wherein the binary indicator (MA(k)) of normality/abnormality of the detected muscle activity pattern is preferably obtained from the score (MAScore) which quantifies a similarity between the detected muscle activity pattern (MAP(k)) and the standard muscle behaviour model (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; the extracted triggers were transmitted to a computation block that assigns, in correspondence of every MT onset, a statistics coherence coefficient between the step to be evaluated and the general muscles behavior during the steady walking),
in particular by comparison with a statistical threshold, the threshold being preferably linked to the previous history of the scores (MAScore) of normality/abnormality of the muscle activity pattern (see [Page 13, F. Logical Conditions Network]; the system extracts the 5th percentile of evaluated muscular scores as the initial threshold and compares each new MT contraction to the initial threshold, wherein if the detected muscular score < the initial threshold, the muscular side activates an alert).
Regarding claim 8, Mezzina teaches the method according to Claim 1, wherein the standard muscle behaviour model is generated from a plurality of MAP muscle activity patterns obtained from signals acquired in absence of a loss of balance (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; the system studied the occurrence of a specific logic state for each single trigger in correspondence of the MT onset, this process leads to the definition of a vector F1R ∊ ℝTr, with "1" the selected state, "R" the reference to the considered MT),
which MAPs are preferably collected and analysed statistically in order to extract a set of weights related to the occurrence of contraction of each selected muscle (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; weight vectors F1R, F0R, F1L, and F0L are defined and applied at every MT contraction generating a step-by-step MAP).
Regarding claim 9, Mezzina teaches the method according to Claim 1, wherein the standard behaviour model (SBM) is updated periodically based on a plurality of previously extracted muscle activity patterns (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; the extracted triggers were transmitted to a computation block that assigns, in correspondence of every MT onset, a statistics coherence coefficient between the step to be evaluated and the general muscles behavior during the steady walking).
Regarding claim 10, Mezzina teaches the method according to Claim 1, wherein at least two indicators of normality/abnormality of the subject's generalized cortical response to said muscular activity, preferably at least three or four generalized cortical response indicators, each representative of the normality/abnormality of a generalized cortical response over a respective cortical macro-area, are used in the classification step (see [Page 13, F. Logical Conditions Network]; system statistically derives thresholds and a network of logical conditions to detect the loss of balance induced by the slippage, wherein the system considers an initial observation window and extracts the 95th percentile of the m values for each band of interest, averaged on 4 functional groups of EEG channels: supplemental motor area, motor area, sensory-motor area, and parietal area).
Regarding claim 11, Mezzina teaches the method according to Claim 10, wherein said cortical macro-areas include one or more, preferably all, of the following cortical macro-areas: supplementary motor area, motor area, sensory-motor area and parietal area (see [Page 13, F. Logical Conditions Network]; averaged on 4 functional groups of EEG channels: supplemental motor area, motor area, sensory-motor area, and parietal area).
Regarding claim 12, Mezzina teaches the method according to Claim 1, wherein the brain signals (SEEG) include a plurality of signals each obtained from a channel for monitoring the motor area, supplementary motor area and/or sensory-motor area, preferably from at least thirteen channels, in particular two or more and preferably all of the following channels: F3, Fz, F4, C3, Cz, C4, Cp5, Cp1 Cp2, Cp6, P3, Pz and P4 (see [Page 11, B. Experimental Setup]; 13 EEG sites were monitored: F3, Fz, F4, C3, Cz, C4, Cp5, Cpl Cp2, Cp6, P3, Pz, P4).
Regarding claim 13, Mezzina teaches the method according to Claim 1, wherein each brain signal (SEEG) received is preliminarily processed by means of a time frequency analysis with sliding windows and/or by means of band multiplexing in a plurality of predefined frequency bands of interest, wherein the bands of interest include one or more, preferably all, of the following frequency bands: 0 (4-7 Hz), a (8-12 Hz), 3 1(13-15 Hz), 3II (16-20 Hz), and 3 111(21-40 Hz) (see [Page 13, E. The EEG Computation Branch, 2) Cortical Responses Analysis]; for the EEG block, the system computes the FFT and extracts the energy of the PSD in 5 bands of interest: 4-7 Hz, 8-12 Hz, 13-15, 15-20, 18-28 Hz).
Regarding claim 14, Mezzina teaches the method according to Claim 1, wherein a first level cortical response indicator (m) is extracted for each channel monitored by the brain signals (SEEG) and preferably for each frequency band of interest, wherein extraction is performed preferably by means of a linear estimation algorithm, in particular least squares algorithm (see [Page 13, E. The EEG Computation Branch, 2) Cortical Responses Analysis]; for each band of interest a linear model of the measured data is extracted via ordinary leads squares fitting, the slope of this model m is then used to describe the cortical responsiveness).
Regarding claim 15, Mezzina teaches the method according to Claim 14, wherein a lateralization indicator is generated from said extracted first level cortical response indicators (m) (see [Page 13, E. The EEG Computation Branch, 2) Cortical Responses Analysis]; for each band of interest a linear model of the measured data is extracted via ordinary leads squares fitting, the slope of this model m is then used to describe the cortical responsiveness),
wherein in particular two overall cortical response parameters, of the right and left side respectively, are derived from the first level cortical response indicators respectively extracted from channels on the right side and left side with respect to the median cortical line (see [Page 13, E. The EEG Computation Branch, 2) Cortical Responses Analysis]; a total of 65 m values may be derived per MT activation reflecting the cortical responsiveness across 13 channels which span the right and left side with respect to the median cortical line and 5 bands of interest).
Regarding claim 16, Mezzina teaches the method according to Claim 1, wherein the one or more generalized cortical response indicators and/or the at least one cortical response lateralization indicator used in the classification step are binary indicators and/or are generated for each band of a plurality of frequency bands of interest (see [Page 13, E. The EEG Computation Branch, 2) Cortical Responses Analysis]; for each band of interest a linear model of the measured data is extracted via ordinary leads squares fitting, the slope of this model m is then used to describe the cortical responsiveness, a total of 65 m values are generated per MT activation, 13 channels * 5 bands of interest).
Regarding claim 17, Mezzina teaches the method according to Claim 1, wherein the classification step is carried out by a logical classifier with at least three levels, wherein a signal indicating an imminent loss of balance is generated if a first level (CL1) detects a presence of anomalies in a generalized cortical response in one or more macro-areas (see [Page 13, F. Logical Conditions Network]; 95th percentile of the m values for each band of interest is averages on 4 functional groups of EEG channels: supplementary motor area, motor area, sensory-motor area, parietal area; and if the m values in the functional group and for each band of interest overcome the initial thresholds, the EEG side activates the corresponding alert),
a second level (CL2) detects a presence of one or more abnormal non-lateralized cortical responses (see [Page 13, F. Logical Conditions Network]; the architecture will enable a potential feedback procedure if there is a parallel presence of a muscular alert and at least 51% of the available EEG warnings, wherein the at least 51% of available EEG warnings indicated that the abnormality is non-lateral), and
a third level detects a simultaneous abnormality of the muscle activation pattern (see [Page 13, F. Logical Conditions Network]; third level - 5th percentile of evaluated muscle scores is set as initial threshold and if the detected muscular score is < the initial threshold, the muscular side activates an alert).
Regarding claim 18, Mezzina teaches the method according to Claim 1, wherein the cortical response indicators, the at least one indicator of MAP normality/abnormality and/or said signal (Aout) indicating an imminent loss of balance are generated for each contraction of a reference muscle detected by the analysis and processing of one or more of said electromyographic signals (SEMG) (see [Page 13, F. Logical Conditions Network]; once the initial thresholds are extracted, the system waits for a new MT contraction to compare to the initial threshold and generate an alert if the detected score is < the initial threshold).
Regarding claim 19, Mezzina teaches the method according to Claim 1, comprising detecting, by means of analysis and processing of one or more of said electromyographic signals (SEMG), one or more contractions of a reference muscle among the selected muscles (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; the extracted triggers were transmitted to a computation block that assigns, in correspondence of every MT onset, a statistics coherence coefficient between the step to be evaluated and the general muscles behavior during the steady walking), and
defining a reference muscle contraction signal (MT) such that each k-th contraction detected identifies an elementary timing unit for the analysis and processing of electromyographic signals (SEMG) and brain signals (SEEG) and/or for said classification (see [Page 12, D. The EMG Computation Branch, 2) Muscular Activity Pattern and Score Assignment]; a routine names Query interrogates the state of each evaluated trigger starting from the MT onset for a time interval of 20 ms, [Page 13, E. The EEG Computation Branch, 1) Online Artifacts Rejection]; the detection of the lateral gastrocnemius, which is the MT, rising edge, enables the EEG processing branch which extracts a 1.04s long EEG time window across the MT as 1 sec before and 0.04 sec after the trigger),
wherein preferably the reference muscle contraction signal is generated bilaterally for both a right side reference muscle contraction and a left side reference muscle contraction and/or the reference muscle is the lateral gastrocnemius (see [Page 13, E. The EEG Computation Branch, 1) Online Artifacts Rejection]; the detection of both sides of the lateral gastrocnemius rising edge enables the EEG processing branch). See also Fig. 3.
Regarding claim 20, Mezzina teaches the method according to Claim 19, wherein the analysis and processing of the plurality of brain signals (SEEG) is initiated by the reference muscle signal (MT) generated in response to a contraction of the reference muscle detected by the analysis and processing of one or more of said electromyographic signals (SEMG) (see [Page 13, E. The EEG Computation Branch, 1) Online Artifacts Rejection]; the detection of both sides of the lateral gastrocnemius rising edge enables the EEG processing branch, see also Fig. 3).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Annese et al (V. F. Annese, M. Crepaldi, D. Demarchi and D. De Venuto, "A digital processor architecture for combined EEG/EMG falling risk prediction," 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, 2016, pp. 714-719) which teaches a digital processor architecture for combined EEG/EMG falling risk prediction.
Plotnik-Peleg et al (US 20140303508 A1) which teaches a system for freezing of gait detection, prediction, and/or treatment.
Guez et al (US 20170006931 A1) which teaches an injury mitigation system and method using adaptive fall and collision detection.
Mohamed Elmahdy et al (US 20200187829 A1) which teaches a system for fall prediction based on electroencephalography and gait analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALISHA J SIRCAR whose telephone number is (571)272-0450. The examiner can normally be reached Monday - Thursday 9-6:30, Friday 9-5:30 CT.
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/A.J.S./
Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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