Smart-Pill Tracking in the Gut

Team Members: Saransh Sharma, Khalil B. Ramadi, Nikhil H. Poole, Shriya S. Srinivasan, Keiko Ishida, Johannes Kuosmanen, Josh Jenkins, Fatemeh Aghlmand, Margaret B. Swift, Mikhail G. Shapiro, Giovanni Traverso, Azita Emami

The advancement in medical technology has led to the development of innovative diagnostic and treatment methods for gastrointestinal (GI) disorders. A key area of focus in this field is the localization and tracking of ingestible smart pills, which are invaluable tools in the diagnosis and management of various GI conditions. In our latest project, we have made significant strides in this area by developing a sophisticated system for the wireless 3D tracking of smart pills within the GI tract. This system is designed to operate in real-time and offers millimeter-scale resolution, marking a significant improvement in tracking capabilities.

The core of our system lies in the generation of 3D magnetic field gradients within the GI field of view. This is accomplished using high-efficiency planar electromagnetic coils, which are ingeniously designed to encode each point in space with a unique magnetic field magnitude. This encoding ensures that every spatial location within the GI tract can be precisely identified based on its distinct magnetic field characteristics.

The smart pills, central to this system, are miniaturized marvels of engineering. They are equipped with low-power, wireless technology that enables them to measure the magnetic field magnitude in their immediate vicinity accurately. As these smart pills navigate through the complex environment of the GI tract, they continuously transmit data regarding the field magnitude they encounter. This transmission allows for the decoding of their exact location within the GI tract, providing real-time tracking as they progress through the digestive system.

The potential applications of this system are vast and impactful. It could play a crucial role in monitoring conditions such as constipation and incontinence, offering a quantitative assessment of GI transit time. Furthermore, the precision of this tracking system opens up new possibilities for targeted therapeutic interventions, allowing for more effective and minimally invasive procedures. The ability to accurately track the movement and location of smart pills within the GI tract not only enhances diagnostic capabilities but also allows for a more personalized approach to treatment.

Our project represents a significant breakthrough in the field of medical technology, particularly in the diagnosis and treatment of GI disorders. The development of a system for the real-time, 3D tracking of ingestible smart pills with millimeter-scale resolution has the potential to revolutionize the way GI disorders are diagnosed and managed. By providing precise and real-time data, this system offers new insights into GI function and pathology, paving the way for more effective, targeted, and minimally invasive treatment options.




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Wireless Surgical Navigation

Team Members: Saransh Sharma, Azita Emami

In our groundbreaking project, we have successfully developed a state-of-the-art radiation-free system specifically designed to enhance the accuracy and safety of various precision surgical procedures. This innovative system is engineered for high-precision alignment, navigation, and tracking of both sensors and surgical tools, offering a revolutionary alternative to traditional methods.

Central to our system is the generation of 3D magnetic field gradients within a designated field-of-view. This sophisticated technique ensures that each point in space is associated with a distinct magnetic field value, creating a detailed and dynamic map of the surgical area. We have meticulously designed and constructed highly miniaturized devices that are both wireless and battery-less. These advanced devices possess the capability to measure their local magnetic field accurately, thereby enabling them to detect and respond to the gradient field.

A key feature of our system is its dual-device setup. One device can be seamlessly attached to an implant within the patient’s body, while another device can be affixed to a surgical tool. This dual functionality allows both devices to simultaneously measure and relay information about the magnetic field at their respective locations to an external display receiver. This real-time communication ensures continuous and precise monitoring during surgery.

Our system has undergone extensive testing, demonstrating an exceptional level of localization accuracy, with measurements achieving less than 100μm in 3D. This level of precision is among the highest reported in the field and represents a significant leap forward in surgical navigation technology.

One of the most significant benefits of our system is its ability to replace the harmful ionizing X-ray radiation traditionally used in precision surgeries. By eliminating the need for X-ray radiation for tracking surgical tools and implants, our system not only enhances the safety of the procedures but also reduces the potential health risks to both patients and medical staff.

Our radiation-free system for surgical alignment, navigation, and tracking represents a transformative development in medical technology. Its unparalleled precision, coupled with its radiation-free nature, positions it as an invaluable tool in the realm of precision surgeries. This advancement not only promises to improve surgical outcomes but also sets a new standard in patient care, emphasizing safety, accuracy, and innovation.



Related Publications

  • S. Sharma et al., “Wireless 3D Surgical Navigation and Tracking System With 100μm Accuracy Using Magnetic-Field Gradient-Based Localization,” in IEEE Transactions on Medical Imaging, vol. 40, no. 8, pp. 2066-2079, Aug. 2021, doi: 10.1109/TMI.2021.3071120.
  • S. Sharma et al., “3D Surgical Alignment with 100µm Resolution Using Magnetic-Field Gradient-Based Localization,” 2020 IEEE International Solid-State Circuits Conference – (ISSCC), San Francisco, CA, USA, 2020, pp. 318-320, doi: 10.1109/ISSCC19947.2020.9063108.

3D CMOS Magnetic Sensor

Team Members: Saransh Sharma

The 3D CMOS Magnetic Sensor represents a significant advancement in the field of magnetic sensing, which is integral to a wide array of applications across various industries, including automotive, navigation, medical electronics, and consumer products. Traditional Hall sensors, though compatible with CMOS technology, often grapple with issues such as subpar sensitivity and high power consumption. Our newly developed 3D magnetic sensor addresses these challenges by combining high sensitivity with ultra-low power operation, all within the standard CMOS process.

This innovative sensor is constructed with three orthogonal metal coils that are densely packed to enhance responsiveness. These coils are designed to generate a voltage in the presence of AC magnetic fields through electromagnetic induction. The resultant voltage signal is then meticulously processed by sophisticated on-chip circuitry. This circuitry is meticulously engineered to perform several critical functions: low-noise amplification to boost signal strength, filtering to eliminate unwanted noise, accurate peak detection, and efficient digitization. Remarkably, all these operations are achieved with an impressively low power consumption of just 14.8µW, enabling the sensor to detect magnetic fields at µT-level sensitivity.

The versatility of the 3D CMOS Magnetic Sensor makes it suitable for a broad spectrum of applications, particularly in scenarios that necessitate AC field sensing. In the biomedical realm, this sensor is poised to revolutionize various procedures and treatments. Its precision and sensitivity make it ideal for tracking catheters and guidewires during intricate endovascular procedures, thereby enhancing safety and outcomes. Additionally, its application in minimally invasive surgeries can lead to more accurate interventions, while its use in targeted radiotherapy can help in delivering treatment more precisely to the affected areas. Furthermore, the sensor’s capabilities extend to serving as fiducial markers in preoperative planning, providing critical data to guide surgical strategies.

In summary, the 3D CMOS Magnetic Sensor represents a leap forward in magnetic sensing technology. Its exceptional sensitivity, coupled with ultra-low power consumption, sets a new standard in the field. The sensor’s potential impact on medical electronics, particularly in improving the accuracy and safety of various medical procedures, underscores its significance. As we continue to explore and refine its applications, the sensor is poised to become an indispensable tool in numerous fields, driving innovation and enhancing capabilities across industries.



Related Publications

  • S. Sharma, H. Melton, L. Edmonds, O. Addington, M. Shapiro and A. Emami, “A Monolithic 3D Magnetic Sensor in 65nm CMOS with <10μTrms Noise and 14.8μW Power,” 2023 IEEE Custom Integrated Circuits Conference (CICC), San Antonio, TX, USA, 2023, pp. 1-2, doi: 10.1109/CICC57935.2023.10121313.

CMOS Fluorescence Sensor

Team Members: Fatima Aghlmand, Saransh Sharma

The development of a novel “Cell-Silicon” system, which fuses silicon chip technology with live bacterial biosensors, presents groundbreaking possibilities in the realms of smart medicine and environmental monitoring. This integrated approach aims to harness the unique capabilities of both silicon-based and biological sensing elements, thereby opening the door to a host of innovative applications.

A critical aspect of such systems is the necessity for on-chip optical filtering, particularly within the wavelength range that aligns with fluorescent proteins. These proteins are commonly employed as signal reporters in bacterial biosensors due to their ability to produce easily detectable fluorescence. However, a significant challenge arises from the fact that the operational range of existing technologies often fails to effectively detect signals emitted by these fluorescent proteins.

Addressing this challenge, our work introduces a fully integrated fluorescence sensor fabricated using 65nm standard Complementary Metal-Oxide-Semiconductor (CMOS) technology. This sensor is a comprehensive solution, incorporating on-chip bandpass optical filters, photodiodes, and dedicated processing circuitry. The design and implementation of this sensor enable it to precisely measure the dynamic fluorescent signals as well as monitor the growth patterns of living Escherichia coli (E. coli) bacterial cells.

One of the most innovative aspects of this research is the utilization of optogenetic techniques. By employing these methods, we have successfully demonstrated a proof of concept for establishing bidirectional communication between living cells and the CMOS chip. This breakthrough indicates the potential for not only monitoring but also controlling biological processes in real-time through electronic interfaces.

The significance of this integrated “Cell-Silicon” system extends far beyond its immediate applications. It lays the groundwork for the development of advanced closed-loop therapeutic solutions, wherein real-time biological feedback can inform and adjust treatment protocols. This technology heralds a new era in personalized medicine, where treatments can be dynamically tailored based on the patient’s biological responses, potentially improving efficacy and reducing side effects. Additionally, in environmental monitoring, this system could provide real-time, on-site analysis of biological markers, offering rapid and accurate assessments of environmental health. The possibilities are vast, and this innovative fusion of biological and silicon-based sensing technologies represents a critical step forward in the intersection of biotechnology and electronics.


Related Publications:

  • F. Aghimand, C. Hu, S. Sharma, K. K. Pochana, R. M. Murray and A. Emami, “A 65nm CMOS Living-Cell Dynamic Fluorescence Sensor with 1.05fA Sensitivity at 600/700nm Wavelengths,” 2023 IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA, 2023, pp. 312-314, doi: 10.1109/ISSCC42615.2023.10067325.
  • F. Aghlmand, C. Y. Hu, S. Sharma, K. Pochana, R. M. Murray and A. Emami, “A 65-nm CMOS Fluorescence Sensor for Dynamic Monitoring of Living Cells“, in IEEE Journal of Solid-State Circuits, vol. 58, no. 11, pp. 3003-3019, Nov. 2023, doi: 10.1109/JSSC.2023.3308853.

Wearable Biosensor for Fatigue Monitoring

Team Members: Saransh Sharma

To effectively monitor the health and performance of military personnel under challenging conditions such as extreme weather, resource scarcity, unsanitary environments, and exposure to exotic diseases, we propose the development of an advanced wearable biosensor patch. This innovative device is designed to continuously track a comprehensive range of biomarkers directly from human sweat. It focuses on three primary categories: metabolic biomarkers, vital signs parameters, and immune response biomarkers.

The wearable biosensor leverages cutting-edge technology to analyze these biomarkers in real-time. By integrating machine learning algorithms, the device can accurately predict fatigue levels, providing a crucial and timely understanding of the physical state of military forces, sailors, and athletes. This predictive capability is pivotal in enhancing overall performance and plays a significant role in risk management and injury prevention strategies.

We are currently in the process of designing the sensor patch, which features a high-performance, low-power integrated circuit (IC) chip. This chip is equipped with functionalities critical for efficient biosensing, including multi-channel signal acquisition, advanced data processing capabilities like chopping, amplification, filtering, digitization, and seamless wireless data communication.

The next phase of our project involves rigorous testing and validation. We plan to conduct in vivo trials with human subjects to assess the biosensor’s accuracy, reliability, and practical applicability in real-world scenarios. Through these trials, we aim to fine-tune the device’s functionality and ensure it meets the high standards required for deployment in military and athletic settings. The ultimate goal is to create a reliable, non-invasive tool that offers real-time insights into the physiological condition of individuals operating in demanding environments, thereby contributing significantly to their health, safety, and performance optimization.



3D Nvigation for High-Precision Surgery

A 3D localization system using magnetic field gradients that can replace X-Ray fluoroscopy in high precision surgery has been developed by our team. Monotonically varying magnetic fields encode spatial points uniquely in the field-of-view and are sensed by miniaturized devices with wireless power and data telemetry. Relative device locations are displayed in real-time. A prototype system consisting of a 65nm CMOS chip and gradient coils achieves a localization error of <100µm in 3D when tested in-vitro.

Brain-Machine Interfaces

Team Members: Benyamin A. Haghi, Sahil Shah, Spencer Kellis from Richard Andersen’s lab.

In the United States, there are about 17,700 new cases per year of Spinal Cord Injury (SCI). SCI results in a partial or total loss of motor function. Brain-Machine Interfaces (BMI) have the potential to increase independence and improve quality of life in SCI patients by reading out neural signals and mapping them onto control signals for assistive devices.

BMI systems serve as an interface between the cortex and peripheral devices and hence they need to be robust over time in the face of different sources of variability. For example, electric potentials in the cortex have small amplitudes and are susceptible to noise, and electrical and mechanical properties of implanted microelectrodes change over time. Neuronal populations may also change over time. Hence, the decoders designed for a BMI system should be able to generalize across these sources of variability to accurately infer movement commands from changing neural signals.

In addition, almost all existing BMI systems run on a desktop computer consuming several watts of power (a typical desktop consumes 60 to 300 watts of power). Such a system is not optimized for real-time processing outside of a clinical setting. Hence, the need for a robust and efficient learning system implemented on an ASIC. Moreover, the algorithms used for such a BMI system have assumed a linear relation between inputs and outputs (e.g., Kalman filters or Wiener filters). In recent years, due to progress made in machine learning and neural networks, there has been an increased interest in adopting these novel techniques for BMI applications. With enough training data, these powerful machine learning algorithms could generalize over large variations in the recorded data.

Our group in collaboration with Richard Andersen’s Lab propose to develop a BMI system which efficiently maps neuronal signal to kinematics in a resource-constrained environment. Figure 1 shows a top-level block diagram of our BMI system.

In our recent work [4] we use neural and behavioral data collected during the open-loop phase of a 2D center-out brain-control task. In this phase of the task, a cursor moves under computer control, with a minimum-jerk velocity profile, from the center of a computer screen to one of eight different target locations arranged uniformly around a unit circle, while the subject uses motor imagery to imagine controlling the cursor. Data is collected in three-minute blocks, each block consisting of 53 trials, with a pseudorandom uniform distribution of targets across trials.

This data is used to train several neural networks. Specifically, a Recurrent Neural Network (RNN), which have shown promising results for sequential data were used. An RNN is composed of feedforward network as well as a feedback network, meaning that all previous outputs are integrated to predict the next time-step RNNs also use previous time step’s input data when computing a new prediction.

While these algorithms are powerful in their capacity to capture complex relationships, they currently require power-hungry computational resources to operate. Part of making BMI systems clinically relevant is to design and develop size- and power-efficient hardware for decoding kinematics such that these systems can be implanted or worn on the body. One of the directions being investigated involves exploring such novel algorithms and energy-efficient hardware.




  1. B. A. Haghi, S. Kellis, S. Shah, M. Ashok, L. Bashford, D. Kramer, B. Lee, Ch. Liu, R. A. Andersen, and A. Emami, “ Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces”2019 Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), 2019, Vancouver, Canada.
    [ PDF ]
  2. B. Haghi, S. Kellis, M. Ashok, S. Shah, L. Bashford, D. Kramer, B. Lee, C. Liu, R. Andersen, A. Emami, “ Deep multi-state dynamic recurrent neural networks for robust brain-machine interfaces”Program No. 406.04. 2019 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2019. Online.
    [ Abstract ]
  3. Benyamin Haghi, Spencer Kellis, Luke Bashford, Sahil Shah, Daniel Kramer, Brian Lee, Charles Liu, Richard Andersen and Azita Emami “Robust Learning Algorithms for Brain Machine Interfaces” IEEE Brain Initiative Workshop on Advanced NeuroTechnologies 2018.
  4. Sahil Shah, Benyamin Haghi, Spencer Kellis, Luke Bashford, Daniel Kramer, Brian Lee, Charles Liu, Richard Andersen and Azita Emami “Decoding Kinematics from Human Parietal Cortex using Neural Networks” International IEEE EMBS Conference on Neural Engineering (Accepted)

Neural Interfaces

Neural interfaces are generally categorized as systems which enable direct communication between the cortex and an external device. Such systems could be used for monitoring and treating neurological disorders like epilepsy, studying and treating neurodegenerative disorders and also for allowing tetraplegic patients to control neuroprosthetic devices. Neural Interfaces will play a vital role in restoring sensory function, communications, and control in impaired humans. Designing low-power circuits and efficient algorithms are essential parts of making these systems robust and wearable.

With respect to Neural Interfaces, our lab focuses on three main areas of research:



Adaptive Deep Brain Stimulation for Parkinson Disease

Team Members: Taige Wang, Maha Shoaran, Benyamin A. Haghi

Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s, with a growing patient population of over 6 million globally. PD mainly affects the motor system, resulting in movement impairments such as muscle rigidity, resting tremor and akinesia. In the early stage, levodopa medication is the most frequently used therapy. Its effectiveness, however, diminishes as the disease progresses and non-dopaminergic brain regions get involved, when surgery-based treatments become inevitable.

Deep Brain Stimulation (DBS) is an established therapy for advanced Parkinson’s disease (PD). It usually targets either the subthalamic nucleus (STN) or internal globus pallidus (GPi) with a constant high-frequency stimulation. DBS leads to an immediate reduction in clinical impairment and improves the UPDRS (Unified Parkinson Disease Rating Scale) motor scores in the long term.

Despite the success of continuous (i.e. open-loop) DBS, its efficacy is limited due to complex programming process, induced side effects, and extensive battery usage. The effectiveness of DBS is highly sensitive to the stimulation parameters such as frequency, pulse width, and intensity, which may take a trained clinician over 6 months to program. In addition, DBS has well known side effects such as speech difficulties and depression, since the normal physiological communication is somewhat suppressed by the stimulation current.

Recent studies have applied the closed-loop control (adaptive DBS or aDBS) using feedback from local field potential (LFP) signals. Despite the success of proof-of-principle studies, aDBS still faces many challenges on its way to clinical therapy. Limited feedback signals and control algorithms, in addition to lack of optimization are among the major obstacles. As an illustration, current aDBS practices focus on simple feedback like beta band power and thresholding, without optimized control or classification algorithms. However, several studies show that beta power in the STN doesn’t correlate with tremor, which suggests that aDBS with only beta power may not properly control all the symptoms. To include more features under extreme hardware resource constraints imposed by the implantable aDBS system, we need to carefully select a set of features with balanced performance and computational resource requirements.

In our group, we study the accuracy of several classifiers. By including relevant features other than beta power, an improvement in accuracy is achieved.  These recent results suggest a great potential to improve current aDBS system for PD, by implementing a classifier with multiple features.

We use state-of-the-art machine learning technique and we study the capacity of several classifiers to face these challenges and investigate the first online motor impairment prediction method. Though some aDBS practices have successfully combined beta activity with inertial sensor or neurochemical recordings as feedback, we focus on the critical information contained in LFPs to avoid the use of additional sensors, lower the power consumption, and achieve reliability.

By including relevant features other than beta power, an improvement in accuracy is achieved.  We test our motor impairment prediction algorithm on each patient. Fig.1 shows some of our recent results. These recent results suggest a great potential to improve current aDBS system for PD, by implementing a classifier with multiple features.


  1. T. Wang, M. Shoaran, and A. Emami, “Toward Adaptive Deep Brain Stimulation in Parkinson’s Disease: LFP-Based Feature Analysis and Classification”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018

Closed-Loop Seizure Control

Team Members: Mahsa Shoaran, Benyamin A. Haghi, Milad Taghavi, Masoud Farivar

Epilepsy is a common neurological disorder affecting over 50 million people in the world. Approximately one third of epileptic patients exhibit seizures that are not controlled by medication. Despite substantial innovations in anti-seizure drug therapy, the proportion of patients with uncontrolled epilepsy has not changed, emphasizing the need for new treatment strategies. The development of new devices capable of performing a rapid and reliable seizure detection followed by brain stimulation holds great promises for improving the quality of life of millions of people with epileptic seizures worldwide.

The high density of neurons in neurobiological tissue requires a large number of electrodes to obtain the most accurate representation of neural activity and provide better control over the location of the stimulation sites or resected epileptic tissue. However, integrating hundreds of acquisition channels at relatively high sampling rates on-chip requires some type of data compression within the sensors to comply with the stringent bandwidth limitations for wireless transmission. In addition, a small size of the implantable system is critical to minimize potential clinical issues associated with implantation, while the total power consumption should be minimized to avoid heat generation inside the brain.

We investigated and evaluated common machine learning methods with focus on hardware complexity and overall performance. We recently developed a closed-loop seizure detection and prevention system. Combined with a novel and efficient feature extraction model, we showed that these classifiers quickly become competitive with more complex learning models, with only a small number of low-depth shallow decision trees. Fig.1 and Fig.3 show the proposed classifier which is fabricated in a 65nm TSMC process, consuming 41.2 nJ/class in a total area of 5401850. It supports 32 iEEG channels, and major frequency and time domain features (such as line length and different frequency bands). The proposed architecture is evaluated in automated seizure detection for epilepsy, using 3074 hours of intracranial EEG data from 26 patients with 393 seizures. Fig.2 show the performance evaluation of the algorithm. Fig.2a shows the average performance vs. number and depth of trees. Fig.2b show the average performance of different classifiers on different patients using iEEG data. Fig.2c shows the feature importance. Finally, latency of the algorithm vs. patient number has been shown on fig.2d. This patient-specific and energy-quality scalable classifier holds great promise for low-power sensor data classification in biomedical applications.


  • Shoaran, B. A. Haghi, M. Taghavi, M. Farivar, and A. Emami, “Energy-Efficient Classification for Resource-Constrained Biomedical Applications”, IEEE Journal on Emerging and Selected  Topics in Circuit and Systems, DOI 10.1109/JETCAS.2018.2844733
  • Taghavi, B. A. Haghi, M. Shoaran, M. Farivar, and A. Emami, “A 41.2 nJ/class, 32-channel on-chip classifier for epileptic seizure detection,” Int. Conf. IEEE Eng. Medicine and Biology Society (EMBC), 2018.
  • Shoaran, B. A. Haghi, M. Farivar, A. Emami, “Efficient Feature Extraction and Classification Methods in Neural Interfaces,” the Bridge, National Academy of Engineering, vol. 47, no. 4, pp. 31-35, Winter 2017.