Wind dispersal of battery-free wireless devices.

Plants cover a large fraction of the Earth's land mass despite most species having limited to no mobility. To transport their propagules, many plants have evolved mechanisms to disperse their seeds using the wind1-4. A dandelion seed, for example, has a bristly filament structure that decreases its terminal velocity and helps orient the seed as it wafts to the ground5. Inspired by this, we demonstrate wind dispersal of battery-free wireless sensing devices. Our millimetre-scale devices weigh 30 milligrams and are designed on a flexible substrate using programmable, off-the-shelf parts to enable scalability and flexibility for various sensing and computing applications. The system is powered using lightweight solar cells and an energy harvesting circuit that is robust to low and variable light conditions, and has a backscatter communication link that enables data transmission. To achieve the wide-area dispersal and upright landing that is necessary for solar power harvesting, we developed dandelion-inspired, thin-film porous structures that achieve a terminal velocity of 0.87 ± 0.02 metres per second and aerodynamic stability with a probability of upright landing of over 95%. Our results in outdoor environments demonstrate that these devices can travel 50-100 metres in gentle to moderate breeze. Finally, in natural systems, variance in individual seed morphology causes some seeds to fall closer and others to travel farther. We adopt a similar approach and show how we can modulate the porosity and diameter of the structures to achieve dispersal variation across devices.

Creating speech zones with self-distributing acoustic swarms.

Imagine being in a crowded room with a cacophony of speakers and having the ability to focus on or remove speech from a specific 2D region. This would require understanding and manipulating an acoustic scene, isolating each speaker, and associating a 2D spatial context with each constituent speech. However, separating speech from a large number of concurrent speakers in a room into individual streams and identifying their precise 2D locations is challenging, even for the human brain. Here, we present the first acoustic swarm that demonstrates cooperative navigation with centimeter-resolution using sound, eliminating the need for cameras or external infrastructure. Our acoustic swarm forms a self-distributing wireless microphone array, which, along with our attention-based neural network framework, lets us separate and localize concurrent human speakers in the 2D space, enabling speech zones. Our evaluations showed that the acoustic swarm could localize and separate 3-5 concurrent speech sources in real-world unseen reverberant environments with median and 90-percentile 2D errors of 15 cm and 50 cm, respectively. Our system enables applications like mute zones (parts of the room where sounds are muted), active zones (regions where sounds are captured), multi-conversation separation and location-aware interaction.

A Low-power wearable acoustic device for accurate invasive arterial pressure monitoring.

BACKGROUND: Millions of catheters for invasive arterial pressure monitoring are placed annually in intensive care units, emergency rooms, and operating rooms to guide medical treatment decision-making. Accurate assessment of arterial blood pressure requires an IV pole-attached pressure transducer placed at the same height as a reference point on the patient's body, typically, the heart. Every time a patient moves, or the bed is adjusted, a nurse or physician must adjust the height of the pressure transducer. There are no alarms to indicate a discrepancy between the patient and transducer height, leading to inaccurate blood pressure measurements. METHODS: We present a low-power wireless wearable tracking device that uses inaudible acoustic signals emitted from a speaker array to automatically compute height changes and correct the mean arterial blood pressure. Performance of this device was tested in 26 patients with arterial lines in place. RESULTS: Our system calculates the mean arterial pressure with a bias of 0.19, inter-class correlation coefficients of 0.959 and a median difference of 1.6 mmHg when compared to clinical invasive arterial measurements. CONCLUSIONS: Given the increased workload demands on nurses and physicians, our proof-of concept technology may improve accuracy of pressure measurements and reduce the task burden for medical staff by automating a task that previously required manual manipulation and close patient surveillance.

Arterial catheters are commonly inserted in hospitalized, critically ill patients to measure blood pressure. For these systems to work properly, a device that measures pressure, called a pressure transducer, must be connected to the catheter, and maintained at the same height as a reference point, usually the heart. So, if the patient moves, the transducer must be manually adjusted by a nurse of physician, adding to the workload of busy clinicians. If not adjusted, this will lead to inaccurate blood pressure measurements. We built a low-power wearable tracking device that uses inaudible acoustic signals to track changes in the patient's position. These height differences can be used to calculate accurate blood pressure measurements automatically. This device can decrease clinician workload by removing the need to move the transducer by hand, allowing providers to focus on other tasks.

Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation.

Background: Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored. Objective: The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data. Methods: We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0-2 days, ±3-7 days, and ±8-30 days from AF events to determine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively. Results: We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence interval [CI] 0.759-0.760), sensitivity of 0.703 (95% CI 0.700-0.705), specificity of 0.684 (95% CI 0.678-0.685), and accuracy of 69.4% (95% CI 0.692-0.700). Model performance was better on ±0-2 day samples (sensitivity 0.711; 95% CI 0.709-0.713) and worse on the ±8-30 day window (sensitivity 0.688; 95% CI 0.685-0.690), with performance on the ±3-7 day window falling in between (sensitivity 0.708; 95% CI 0.704-0.710). Conclusion: Neural networks can predict AF using a widely scalable and cost-effective mobile technology prospectively and retrospectively.

Solar-powered shape-changing origami microfliers.

Using wind to disperse microfliers that fall like seeds and leaves can help automate large-scale sensor deployments. Here, we present battery-free microfliers that can change shape in mid-air to vary their dispersal distance. We designed origami microfliers using bistable leaf-out structures and uncovered an important property: A simple change in the shape of these origami structures causes two dramatically different falling behaviors. When unfolded and flat, the microfliers exhibit a tumbling behavior that increases lateral displacement in the wind. When folded inward, their orientation is stabilized, resulting in a downward descent that is less influenced by wind. To electronically transition between these two shapes, we designed a low-power electromagnetic actuator that produces peak forces of up to 200 millinewtons within 25 milliseconds while powered by solar cells. We fabricated a circuit directly on the folded origami structure that includes a programmable microcontroller, a Bluetooth radio, a solar power-harvesting circuit, a pressure sensor to estimate altitude, and a temperature sensor. Outdoor evaluations show that our 414-milligram origami microfliers were able to electronically change their shape mid-air, travel up to 98 meters in a light breeze, and wirelessly transmit data via Bluetooth up to 60 meters away, using only power collected from the sun.

Micro-mechanical blood clot testing using smartphones.

Frequent prothrombin time (PT) and international normalized ratio (INR) testing is critical for millions of people on lifelong anticoagulation with warfarin. Currently, testing is performed in hospital laboratories or with expensive point-of-care devices limiting the ability to test frequently and affordably. We report a proof-of-concept PT/INR testing system that uses the vibration motor and camera on smartphones to track micro-mechanical movements of a copper particle. The smartphone system computed the PT/INR with inter-class correlation coefficients of 0.963 and 0.966, compared to a clinical-grade coagulation analyzer for 140 plasma samples and demonstrated similar results for 80 whole blood samples using a single drop of blood (10 µl). When tested with 79 blood samples with coagulopathic conditions, the smartphone system demonstrated a correlation of 0.974 for both PT/INR. Given the ubiquity of smartphones in the global setting, this proof-of-concept technology may provide affordable and effective PT and INR testing in low-resource environments.

Performing tympanometry using smartphones.

Background: Tympanometry is used as part of a battery of tests for screening of middle ear function and may help diagnose middle ear disorders, but remains available only on expensive test equipment. Methods: We report a low-cost smartphone-based tympanometer system that consists of a lightweight and portable attachment to vary air pressure in the ear and measure middle ear function. The smartphone displays a tympanogram and reports peak acoustic admittance in realtime. Our programmable and open-source system operates at 226 Hz and was tested on 50 pediatric patient ears in an audiology clinic in parallel with a commercial tympanometer. Results: Our study shows an average agreement of 86 ± 2% between the 100 tympanograms produced by the smartphone and commercial device when five pediatric audiologists classified them into five classes based on the Liden and Jerger classification. Conclusion: Given the accessibility and prevalence of budget smartphones in developing countries, our open-source tool may help provide timely and affordable screening of middle ear disorders.

An off-the-shelf otoacoustic-emission probe for hearing screening via a smartphone.

Otoacoustic emissions (OAEs) provide information about the function of the outer hair cells of the cochlea. In high-income countries, infants undergo OAE tests as part of the screening protocols for hearing. However, the cost of the necessary equipment hinders early screening for hearing in low- and middle-income countries, which disproportionately bear the brunt of disabling hearing loss. Here we report the design and clinical testing of a low-cost probe for OAEs. The device, which has a material cost of approximately US$10, uses an off-the-shelf microphone and off-the-shelf earphones connected to a smartphone through a headphone jack. It sends two pure tones through each of the headphone's earbuds and algorithmically detects the distortion-product OAEs generated by the cochlea and recorded via the microphone. In a clinical study involving 201 paediatric ears across three healthcare sites, the device detected hearing loss with 100% sensitivity and 88.9% specificity, comparable to the performance of a commercial device. Low-cost devices for OAE testing may aid the early detection of hearing loss in resource-constrained settings.

Using smart speakers to contactlessly monitor heart rhythms.

Heart rhythm assessment is indispensable in diagnosis and management of many cardiac conditions and to study heart rate variability in healthy individuals. We present a proof-of-concept system for acquiring individual heart beats using smart speakers in a fully contact-free manner. Our algorithms transform the smart speaker into a short-range active sonar system and measure heart rate and inter-beat intervals (R-R intervals) for both regular and irregular rhythms. The smart speaker emits inaudible 18-22 kHz sound and receives echoes reflected from the human body that encode sub-mm displacements due to heart beats. We conducted a clinical study with both healthy participants and hospitalized cardiac patients with diverse structural and arrhythmic cardiac abnormalities including atrial fibrillation, flutter and congestive heart failure. Compared to electrocardiogram (ECG) data, our system computed R-R intervals for healthy participants with a median error of 28 ms over 12,280 heart beats and a correlation coefficient of 0.929. For hospitalized cardiac patients, the median error was 30 ms over 5639 heart beats with a correlation coefficient of 0.901. The increasing adoption of smart speakers in hospitals and homes may provide a means to realize the potential of our non-contact cardiac rhythm monitoring system for monitoring of contagious or quarantined patients, skin sensitive patients and in telemedicine settings.

Closed-loop wearable naloxone injector system.

Overdoses from non-medical use of opioids can lead to hypoxemic/hypercarbic respiratory failure, cardiac arrest, and death when left untreated. Opioid toxicity is readily reversed with naloxone, a competitive antagonist that can restore respiration. However, there remains a critical need for technologies to administer naloxone in the event of unwitnessed overdose events. We report a closed-loop wearable injector system that measures respiration and apneic motion associated with an opioid overdose event using a pair of on-body accelerometers, and administers naloxone subcutaneously upon detection of an apnea. Our proof-of-concept system has been evaluated in two environments: (i) an approved supervised injection facility (SIF) where people self-inject opioids under medical supervision and (ii) a hospital environment where we simulate opioid-induced apneas in healthy participants. In the SIF (n = 25), our system identified breathing rate and post-injection respiratory depression accurately when compared to a respiratory belt. In the hospital, our algorithm identified simulated apneic events and successfully injected participants with 1.2 mg of naloxone. Naloxone delivery was verified by intravenous blood draw post-injection for all participants. A closed-loop naloxone injector system has the potential to complement existing evidence-based harm reduction strategies and, in the absence of bystanders, help make opioid toxicity events functionally witnessed and in turn more likely to be successfully resuscitated.

Wireless steerable vision for live insects and insect-scale robots.

Vision serves as an essential sensory input for insects but consumes substantial energy resources. The cost to support sensitive photoreceptors has led many insects to develop high visual acuity in only small retinal regions and evolve to move their visual systems independent of their bodies through head motion. By understanding the trade-offs made by insect vision systems in nature, we can design better vision systems for insect-scale robotics in a way that balances energy, computation, and mass. Here, we report a fully wireless, power-autonomous, mechanically steerable vision system that imitates head motion in a form factor small enough to mount on the back of a live beetle or a similarly sized terrestrial robot. Our electronics and actuator weigh 248 milligrams and can steer the camera over 60° based on commands from a smartphone. The camera streams "first person" 160 pixels-by-120 pixels monochrome video at 1 to 5 frames per second (fps) to a Bluetooth radio from up to 120 meters away. We mounted this vision system on two species of freely walking live beetles, demonstrating that triggering image capture using an onboard accelerometer achieves operational times of up to 6 hours with a 10-milliamp hour battery. We also built a small, terrestrial robot (1.6 centimeters by 2 centimeters) that can move at up to 3.5 centimeters per second, support vision, and operate for 63 to 260 minutes. Our results demonstrate that steerable vision can enable object tracking and wide-angle views for 26 to 84 times lower energy than moving the whole robot.

Opioid overdose detection using smartphones.

Early detection and rapid intervention can prevent death from opioid overdose. At high doses, opioids (particularly fentanyl) can cause rapid cessation of breathing (apnea), hypoxemic/hypercarbic respiratory failure, and death, the physiologic sequence by which people commonly succumb from unintentional opioid overdose. We present algorithms that run on smartphones and unobtrusively detect opioid overdose events and their precursors. Our proof-of- concept contactless system converts the phone into a short-range active sonar using frequency shifts to identify respiratory depression, apnea, and gross motor movements associated with acute opioid toxicity. We develop algorithms and perform testing in two environments: (i) an approved supervised injection facility (SIF), where people self-inject illicit opioids, and (ii) the operating room (OR), where we simulate rapid, opioid-induced overdose events using routine induction of general anesthesia. In the SIF (n = 209), our system identified postinjection, opioid-induced central apnea with 96% sensitivity and 98% specificity and identified respiratory depression with 87% sensitivity and 89% specificity. These two key events commonly precede fatal opioid overdose. In the OR, our algorithm identified 19 of 20 simulated overdose events. Given the reliable reversibility of acute opioid toxicity, smartphone-enabled overdose detection coupled with the ability to alert naloxone-equipped friends and family or emergency medical services (EMS) could hold potential as a low-barrier, harm reduction intervention.

Contactless cardiac arrest detection using smart devices.

Out-of-hospital cardiac arrest is a leading cause of death worldwide. Rapid diagnosis and initiation of cardiopulmonary resuscitation (CPR) is the cornerstone of therapy for victims of cardiac arrest. Yet a significant fraction of cardiac arrest victims have no chance of survival because they experience an unwitnessed event, often in the privacy of their own homes. An under-appreciated diagnostic element of cardiac arrest is the presence of agonal breathing, an audible biomarker and brainstem reflex that arises in the setting of severe hypoxia. Here, we demonstrate that a support vector machine (SVM) can classify agonal breathing instances in real-time within a bedroom environment. Using real-world labeled 9-1-1 audio of cardiac arrests, we train the SVM to accurately classify agonal breathing instances. We obtain an area under the curve (AUC) of 0.9993 ± 0.0003 and an operating point with an overall sensitivity and specificity of 97.24% (95% CI: 96.86-97.61%) and 99.51% (95% CI: 99.35-99.67%). We achieve a false positive rate between 0 and 0.14% over 82 h (117,985 audio segments) of polysomnographic sleep lab data that includes snoring, hypopnea, central, and obstructive sleep apnea events. We also evaluate our classifier in home sleep environments: the false positive rate was 0-0.22% over 164 h (236,666 audio segments) of sleep data collected across 35 different bedroom environments. We prototype our proof-of-concept contactless system using commodity smart devices (Amazon Echo and Apple iPhone) and demonstrate its effectiveness in identifying cardiac arrest-associated agonal breathing instances played over the air.

Detecting middle ear fluid using smartphones.

The presence of middle ear fluid is a key diagnostic marker for two of the most common pediatric ear diseases: acute otitis media and otitis media with effusion. We present an accessible solution that uses speakers and microphones within existing smartphones to detect middle ear fluid by assessing eardrum mobility. We conducted a clinical study on 98 patient ears at a pediatric surgical center. Using leave-one-out cross-validation to estimate performance on unseen data, we obtained an area under the curve (AUC) of 0.898 for the smartphone-based machine learning algorithm. In comparison, commercial acoustic reflectometry, which requires custom hardware, achieved an AUC of 0.776. Furthermore, we achieved 85% sensitivity and 82% specificity, comparable to published performance measures for tympanometry and pneumatic otoscopy. Similar results were obtained when testing across multiple smartphone platforms. Parents of pediatric patients (n = 25 ears) demonstrated similar performance to trained clinicians when using the smartphone-based system. These results demonstrate the potential for a smartphone to be a low-barrier and effective screening tool for detecting the presence of middle ear fluid.