Self-Powered Load Sensing Circuitry for Total Knee Replacement

Abstract

There has been a significant increase in the number of total knee replacement (TKR) surgeries over the past few years, particularly among active young and elderly people suffering from knee pain. Continuous and optimal monitoring of the load on the knee is highly desirable for designing more reliable knee implants. This paper focuses on designing a smart knee implant consisting of a triboelectric energy harvester and a frontend electronic system to process the harvested signal for monitoring the knee load. The harvester produces an AC signal with peak voltages ranging from 10 V to 150 V at different values of knee cyclic loads. This paper demonstrates the measurement results of a PCB prototype of the frontend electronic system fabricated to verify the functionality and feasibility of the proposed approach for a small range of cycling load. The frontend electronic system consists of a voltage processing unit to attenuate high peak voltages, a rectifier and a regulator to convert the input AC signal into a stabilized DC signal. The DC voltage signal provides biasing for the delta-sigma analog-to-digital converter (ADC). Thus, the output of the triboelectric harvester acts as both the power signal that is rectified/regulated and data signal that is digitized. The power consumption of the proposed PCB design is approximately 5.35 μW. Next, the frontend sensor circuitry is improved to accommodate a wider range of cyclic load. These results demonstrate that triboelectric energy harvesting is a promising technique for self-monitoring the load inside knee implants.

I. Introduction

Continuously monitoring the magnitude and distribution of loads on total knee replacement (TKR) implants would open opportunities in detecting the unsafe loads, improving implant designs, and in responding to the patient before the implant failure occurs. It can also help in post-operative joint monitoring to alert patients and surgeons about problems that could potentially be addressed early to avoid a more complex and expensive surgery later on [1]. There has been significant research in the field of smart knee implants. In [2], a smart knee implant powered by the energy harvested from an electromagnetic generator was developed. This harvester is a combination of several magnets inserted between the coil, which causes large area and consumes power in the range of milliwatts. Knee implant systems described in [3], [4] harvest energy using piezoelectric elements. Since the energy is harvested through deformations, service life of the implant is low. In [5], [6], TKR implants were embedded in the knee of a senior patient. The implant consisted of four load cells with wireless micro-transmitters. However, the major limitation of these designs was that the load measurement could only be performed in a medical clinic as remote powering using magnetic near-field coupling was required for operation. Extensive in vivo studies have also been reported on measuring the load at knee joints during activities of daily living [7], [8]. They provided a detailed description of the forces and moments acting on the knee joints. However, the range of input loads that could be monitored by this implant is highly limited.

The utilization of sensors for intra-and post-operative estimation has received much attention for the field of biomedical research in recent years, particularly in the field of orthopaedics. Such intra-operative estimation sensors are commercially available for the total knee replacement surgeries. Yet, they provide data only during the surgery and should be removed by the end of the surgery.

In [9], a scalable, wearable e-textile triboelectric energy harvesting (WearETE) system has been developed. The WearETE system features low cost material and manufacturing methods, high accessibility, and high feasibility for powering wearable sensors and electronics.

In this research, we incorporate triboelectric energy harvesting into the smart knee implant. The proposed approach offers several advantages such as compactness, customization for various knee implants, ability to be powered only by the harvester, thereby enabling continuous force measurement.

Triboelectric energy harvesting relies on contact electrification and electrostatic induction. A triboelectric generator (TEG) typically consists of two materials (tribolayers) with different polarities. At least one of the materials should be dielectric with a conductor on both sides of the tribolayers. When the two tribolayers are pressed or rubbed against each other, opposite charges are produced, referred to as contact electrification. Due to these charges, the conductor layers are electrostatically induced and can create a flow of electrons, if the tribolayers are periodically moved back and forth. This energy harvesting technology has shown promising results in a wide range of sensor applications [10]–[12], including biomedical systems [13], [14]. In recent work, it was demonstrated that TEG can be used to harvest reasonable power from natural knee motions [15]–[19]. Hence, triboelectric harvesters can be incorporated in designing a self-powered electronic system [10], [20], [21], which can continuously monitor the knee load [17]. Furthermore, triboelectric generators offer several advantages over piezoelectric or electromagnetic based energy harvesting such as higher energy density (313W/m² [10]), simple fabrication at low cost, excellent reliability, high efficiency, and greater sensitivity [22], [23]. Furthermore, since triboelectric transducers generate voltage from pressure (force), they do not require large deformations. As such, they do not have the size limitation of piezoelectric devices. Since knee is one of the body joints that takes 3 to 6 times the body weight, triboelectric energy harvesting could serve as a promising candidate for scavenging energy from the loads at the knee joint, as demonstrated in this paper.

The schematic of the proposed total knee replacement (TKR) system consisting of the femoral and tibial tray and the ultra high mechanical polyethylene (UHMWPE) bearing parts is illustrated in Fig. 1(a). The designed triboelectric harvesters are placed between the tibial tray and the UHMWPE bearing for perfect load monitoring. The proposed frontend electronic system would be placed on the tibial tray and powered entirely by the power generated by the designed triboelectric harvesters without external biasing. The harvesters and the electronic system would be placed in a 3D package [19], [24]. The enlarged view of the package with the triboelectric generators is shown in Fig. 1(b). PDMS has been used while designing the triboelectric harvester because of its biocompatibility and flexibility. FR4 (flame retardant) epoxy glass PCB substrate has been adopted for fabrication as it has high dielectric strength, thereby contributing to its electrical insulation properties. It also has high strength-to-weight ratio and is sufficiently lightweight.

Fig. 1.

Proposed total knee replacement (TKR) system: (a) schematic of the system implanted within the knee, (b) enlarged view of the package with the harvesters.

The primary contributions of this paper include the design of a self-powered frontend electronic circuit to process the output voltage signal of the triboelectric harvester. Both small and wide range of cyclic loads can be processed with sufficient accuracy. The measurement results of the prototype PCBs are presented, demonstrating the promise of triboelectric energy harvesting for self-monitoring the load inside knee implants.

The rest of the paper is organized as follows. A brief overview of the harvester design and fabrication is provided in Section II. The frontend sensor circuitry for both small and wide range of cyclic loads is described in Section III. The fabricated PCB prototype, the experimental setup and measurement results are also presented in this section. Finally, the paper is concluded in Section IV.

II. Harvester Design and Fabrication

Vertical contact mode triboelectric energy harvester has been used to generate the AC voltage signal from cyclic contact and separation motions. For providing the necessary contact and separation motions, the parts of the harvester are fixed inside a mechanical spring-controlled housing. A polyethylene packaged triboelectric harvester prototype from our previous study [25] was used to generate the AC voltage signal from cyclic contact and separation motions. The package serves as housing for the generators and the electronic system together. The prototype’s overall geometry was based on the perimeter shape of a size 7 tibial tray (Triathlon, Stryker, Kalamazoo, MI) that adds approximately 16 mm to the overall height of the tibial component. The upper tribolayer of the harvester, also a metal electrode, was CNC machined from micro-patterned titanium (100 μm sawtooth ridge) and the lower tribolayer was fabricated by spin-coating PDMS mixtures on a back electrode that was machined from a flat titanium. To make PDMS, first, the titanium electrodes are cleaned with acetone and distilled water in an ultrasonic cleaner. Then, the PDMS elastomer base and the curing agent are mixed in 10:1 weight ratio. The mixture is stirred thoroughly and degassed in a vacuum chamber. After degassing, the PDMS paste is spin coated at 500 RPM for 36 seconds on the surface of the flat titanium. Finally, the PDMS coated titanium is cured at 90°C for 45 minutes on a hot plate. The upper and the lower titanium parts closely follow the shape of a standard tibial tray. The TEG and its package were tested for 10,000 cycles and maintained stable outputs under equivalent body loads of 1000 N-2000 N [25]. The assembly of the upper and the lower harvester parts inside the package are shown in Fig. 2.

Fig. 2.

Prototype of the polyethylene packaged harvesters: (a) 3D design view, (b) upper packaged harvester’s part, (c) lower packaged harvester’s part.

The designed harvester produces an AC signal at approximately 1 Hz with a peak voltage that is proportional to the force applied to the plates of the harvester. In this packaged harvester prototype, a single harvester when connected to its optimum resistance (220 MΩ) can provide 15–140 V peak AC voltages and 0.3–26 μW of apparent power (V_rms × I_rms) at 100–2000 N sinusoidal force. For example, as shown in Fig. 3 the harvester produces an AC voltage signal with a peak voltage of 53 V when the packaged prototype is under 500 N of sinusoidal loads. This peak voltage increases to 60 V when the applied force is 600 N. If two harvesters are connected in parallel, 14 μW apparent power is generated at 525 N. This power increases to 17 μW at 600 N. Fig. 4 plots the measured peak harvester voltage for various forces to further demonstrate the dependence of voltage signal on applied force.

Fig. 3.

Voltage signal from the harvester at a force of 500 N and 600 N.

Fig. 4.

Measured peak harvester voltage as a function of applied force.

III. Frontend Sensor Circuitry

The proposed design for small range of cyclic loads is described in Section III-A. The details of the PCB prototype and results are discussed in Section III-B. The frontend circuit for wide range of cyclic loads is presented in Section III-C.

A. Small Range of Cyclic Loads

The architectural block diagram of the frontend electronic system for the smart knee implant is shown in Fig. 5. This system comprises of an Attenuator I, a two-stage LC filter for attenuating the high voltages from the harvester, which is common for both the signal and power paths. The signal path has an additional Attenuator II (consisting of a single stage LC filter and a diode) to further condition the data signal, which is digitized by a successive approximation register (SAR) analog-to-digital converter (ADC). The ADC converts the attenuated input analog signal into digital bits for monitoring the knee load. During testing, the digital output from the ADC is read using the STMicroelectronics evaluation board STM32F407. The power path has a rectifier and regulator to extract the supply voltage from the harvested signal. The rectifier converts the output AC signal of the two-stage LC filter into a DC signal, which is further stabilized and regulated by the regulator. The system can be entirely powered through the power generated by the harvesters without any external bias voltage [15]–[18]. This proposed system has been fabricated on a printed circuit board (PCB) in order to demonstrate the feasibility.

Fig. 5.

Architectural block diagram of the prototype electronic system for small range of cyclic loads.

The high peak voltages in the harvested signal are attenuated into low peak voltages through the Attenuator I, a two-stage LC filter, as shown in Fig. 6. This filter also acts as an impedance matching block to maximize power transfer from the load. The values of the circuit elements are also indicated in the figure. These values are chosen to achieve the desired attenuation (approximately by a factor of 14) while ensuring high power efficiency. The equivalent series resistance (ESR) is considered during the design process for the inductors and capacitors [26], [27].

Fig. 6.

Schematic of attenuator I consisting of a two-stage LC filter to reduce the peak voltages of the harvested signal.

A diode rectifier is utilized along the power path to convert the output AC signal from the two-stage LC filter into a DC voltage. This DC voltage is then passed through a linear dropout regulator to produce a stabilized voltage free from the variations in the input voltage. Along the signal path, an Attenuator II (single stage LC filter and a diode) is used at the output of the two-stage LC filter, as shown in Fig. 7. This additional attenuator is needed since the ADC input signal should be in the range of 0.3 V to 2.8 V whereas the output of the two-stage LC filter lies in the range of −6 V to 6 V. The output of this attenuator is the input data to the SAR ADC for load monitoring. As the input data signal frequency is approximately 1 Hz, the clock frequency for the ADC is chosen to be 10 kHz which provides sufficient accuracy.

Fig. 7.

Schematic of attenuator II along the signal path consisting of a single-stage LC filter and diode rectifier.

B. Fabricated PCB Prototype

The frontend electronic system described above is fabricated on a PCB, as shown in Fig. 8. Larger form factor passive devices and commercially available rectifier, regulator, ADC and diodes are used for this prototyping.

Fig. 8.

PCB prototype of frontend electronic system for small range of cyclic loads.

1). Experimental Setup:

The experimental setup built in order to generate the voltages using the triboelectric generator (TEG) is depicted in Fig. 9. It consists of MTS 858 Servo Hydraulic Test System for conducting cyclic axial load, and a FlexTest controller for the amplitude tuning. The MTS has a built-in load cell for measuring the applied force. The generated voltage signal is captured using a Keithley M6514 and an ExceLINX program [15]–[18].

Fig. 9.

Experimental setup for measurements.

2). Measurement Results:

Table I lists the PCB experimental results at various stages of the system when tested with the triboelectric harvester. It also compares these test results with the ORCAD simulation results. In this experiment, the harvester output signal has a peak voltage of 53 V. A preliminary package is used for testing the harvester. Fig. 10 compares the measured output of the Attenuator I, two-stage LC filter with the ORCAD simulation result. The PCB output signal has a peak of 4 V as compared to the 4.1 V peak signal from the ORCAD simulation. Fig. 11 compares the measured output of the regulator with the simulation result. The PCB output is a 2.4 V DC signal whereas the ORCAD simulation output is a 2.38 V DC signal. Fig. 12 compares the measured data input to the ADC (output of the Attenuator II) with the ORCAD simulation result. The measured signal has a range of 0.1 V to 1.1 V. The ORCAD simulation also produces a signal in the similar range. Finally, Fig. 13 compares the measured output of the ADC with the ORCAD simulation result. In this case, the analog input signal to the ADC is approximately 0.9 V and the output digital data is identical in both cases (11111100). Note that the digitized data will be wirelessly transmitted to an external reader device via inductive coupling. This data telemetry will be powered by the external reader, which remains as future work.

TABLE I.

Comparison of PCB Test and ORCAD Simulation Results for 53 V Peak Harvested Signal

Circuit Component	PCB Test Output	ORCAD Simulation Output
Two-stage LC Filter	4 V peak AC	4.1 V peak AC
Diode Rectifier	3 V DC	3.1 V DC
Regulator	2.4 V DC	2.38 V DC
ADC Data Input	0.1V to 1.1V	−0.1V to 1.11V
SAR ADC (for 0.9 V)	Digital Data 11111100	Digital Data 11111100

Fig. 10.

PCB and ORCAD results for the output of attenuator I.

Fig. 11.

PCB and ORCAD results for the regulator output.

Fig. 12.

PCB and ORCAD results for the ADC input data.

Fig. 13.

PCB and ORCAD results for the output of the ADC.

These test results validate the proposed design as PCB test results and circuit simulation results are sufficiently close. The slight mismatch is primarily due to the disparity between the input signals in each case. Specifically, for simulations, the input signal is produced by the electrical model of the harvester, which approximates the actual signal generated by the harvester.

3). Power Consumption:

The overall system consumes approximately 5.35 μW power. The regulator and SAR ADC contribute the most to overall power consumption. Two harvesters connected in parallel can generate 9 μW power at an applied force of 450 N. Thus, the proposed frontend electronic circuitry can be entirely self-powered.

C. Wide Range of Cyclic Loads

The PCB prototype described above can monitor loads in the range of 450–650 N, corresponding to peak voltages of 45 V to 65 V. In practice, the harvester output ranges from 10 V peak to 150 V peak voltage depending upon the applied force. Thus, linear LC filter based attenuation is not sufficient since the filter output would have a peak voltage of 11.5 V when the harvester output signal has peak voltage of 150 V. Furthermore, LC attenuation requires relatively large passive circuit elements due to low operating frequency of approximately 1 Hz.

The architectural block diagram of the sensor circuitry designed for wide range of cyclic loads is shown in Fig. 14. In this design approach, the signal and power paths do not share a passive filter or attenuator. Instead, a non-linear attenuator is used for the power path, followed with rectification and regulation. For the signal path, a capacitive divider based linear attenuator is used with significantly lower capacitors. These blocks are explained in the following subsections.

Fig. 14.

Architectural block diagram of the sensor circuitry for wide range of cyclic loads.

1). Linear and Non-Linear Attenuator:

The improved electronic circuitry comprises of two attenuators; a linear attenuator along the signal path and a non-linear attenuator along the power path, as shown in Fig. 15. The input impedance of the signal path is significantly larger to minimize current flow into the signal path. The input impedance of the non-linear attenuator is adjusted to match the input impedance of the harvester (≈220 MΩ) to maximize power transfer. Table II lists the values of all the passive components from these two attenuators. The equivalent series resistance (ESR) is also considered for all the capacitors [28], [29]. Along the power path, the signal from the harvester is attenuated through the capacitive divider, C1 and C2 (attenuation factor is 3.5). This signal is passed through the diode rectifier, D2, and capacitor, C6, to provide the biasing for the amplifier, LM358-N, incorporated in the design. The signal from the capacitive divider is attenuated again through the capacitive divider, C3 and C4 (attenuation factor is 1.5), and diode, D1. This step ensures that the input signal to the opamp, Vi, is always less than the bias signal, V+. C5 is a feedback capacitor that provides the desired attenuation between the input and output of the amplifier, as described below. Referring to Fig. 15 and applying KCL, the following expressions are obtained,

$$\frac{Vin-V1}{Z1}\approx \frac{V1-Vi}{Z3}+\frac{V1}{Z2},$$ (1)

$$\frac{V1-Vi}{Z3}\approx \frac{Vi}{Z4}+\frac{Vi-Vout}{Z5},$$ (2)

where Z1, Z2, Z3, Z4 and Z5 are the impedances of the respective capacitances. Note that the current of the diodes D1 and D2 is neglected in (1) and (2). For the amplifier, the characteristic equation is,

$$Vout=A(Vi-Vout),$$ (3)

where A is the gain of the amplifier. Replacing (3) in (2) and rearranging yields,

$$\frac{Vout}{Vin}\approx k1+\frac{k2}{A}+\frac{1}{A(Z5-1)},$$ (4)

where k1 and k2 are functions of impedances Z1 to Z5 and are constant at constant frequency. The amplifier, LM358-N, is a voltage controlled current source where the transconductance (and therefore the open loop gain A) changes with the input voltage [30]. Specifically, as the input voltage increases, the gain decreases, thereby achieving non-linear attenuation at the output of the amplifier. The accuracy of (4) is evaluated by comparing the analytic results with the simulated values (see Fig. 16) for different harvester output voltages. The average error is approximately 1.71%.

Fig. 15.

Schematic of the linear and non-linear attenuators within the sensor circuitry.

TABLE II.

Component Values Within the Linear and Non-Linear Attenuator

S.No.	Component	Value	ESR
1.	C1	1nF	0.01Ω
2.	C2	15nF	0.06Ω
3.	C3	5nF	0.03Ω
4.	C4	10nF	0.04Ω
5.	C5	0.5nF	0.01Ω
6.	C6	20nF	0.07Ω
7.	C1	0.3nF	0.01Ω
8.	C8	15nF	0.06Ω
9.	C9	1nF	0.01Ω

Fig. 16.

Peak linear and non-linear attenuator output vs. peak harvester output.

Along the signal path, linear attenuation is achieved via C7 and C8 (attenuation factor is approximately 62.5), followed with a rectification stage consisting of D3 and C9. This attenuation ensures that the input data to ADC is always less than the supply voltage Vdd of the ADC, which is determined by the power path. Note that the supply voltage of the ADC varies in the range of 1.8 V to 2.8 V depending upon the peak voltage of the harvested signal.

2). Measurement Results:

The frontend electronic system for wide range of cyclic loads is fabricated on a PCB, as shown in Fig. 17. The voltages at the output of the linear and non-linear attenuators are illustrated in Fig. 18 for a harvested signal with a peak voltage Vin of 105 V. The input voltage of the amplifier, Vi has a peak voltage of 3.5 V, as shown in Fig. 18(b). The output voltage of the non-linear attenuator, Vout has a peak value of 3.1 V, as shown in Fig. 18(c) for both simulations and measurements. This signal is converted into a DC voltage for the ADC bias through the rectifier and regulator. The output of the linear attenuator (input data signal for the ADC) has a peak voltage of 1.6 V, as shown in Fig. 18(d) for both simulations and measurements. The measured data is sufficiently close to the simulation results, particularly for the peak voltages.

Fig. 17.

PCB prototype of frontend electronic system for wide range of cyclic loads.

Fig. 18.

Output waveforms of the proposed sensor circuitry: (a) harvester output with 105 V peak voltage Vin, (b) amplifier input voltage Vi, (c) non-linear attenuator output voltage Vout, and (d) linear attenuator output voltage.

The peak output voltages of the linear and non-linear attenuator for different harvested voltages are also plotted in Fig. 16. The output peak voltages for the non-linear attenuator range from 2.5 V to 3.5 V, whereas the output peak voltages of the linear attenuator lie in the range of 0.16 V to 2.4 V. The ADC works properly for this range of bias voltage and input data signals, which are within its resolution range. Thus, the overall circuit can work for a wide range of harvester signals (from 10 V to 150 V peak voltage).

Finally, the minimum change in the harvester voltage that can be sensed by the circuit with sufficient accuracy (i.e. voltage resolution) is characterized. This result is shown in Fig. 19 for various intervals of the peak harvester voltage. The resolution ranges from approximately 12 mV to 21 mV. The worst case resolution corresponds to harvester voltages greater than 111 V. Also, the difference between the digitized ADC output and corresponding harvester voltage is calculated to evaluate accuracy. For this comparison, the ADC output data is multiplied by the overall attenuation factor of the signal path, which is approximately 62.5. The maximum error is 3.48% whereas the average error is 2.83%.

Fig. 19.

Voltage resolution of the proposed sensor circuitry for various intervals of the harvester voltage.

3). Power Consumption:

The overall power consumed by the circuit is approximately 5.1 μW. The regulator and SAR ADC contribute the most to overall power consumption. According to this result, the proposed frontend electronic circuitry can be entirely self-powered by a single harvester for cyclic loads 600 N to 2000 N, corresponding to harvester output peak voltages of 60 V to 140 V. Two harvesters connected in parallel can generate 41–67 μW of power at 1000–1500 N of sinusoidal loads. If the load is less than 600 N, the parallel connection of the harvesters can be utilized or supercapacitors can be used to store excess energy. Note that less power is consumed as compared to the previous design due to the much smaller passive devices with significantly lower ESR values.

IV. Conclusions

Continuous and optimal monitoring of the load is a promising technique in improving the design of knee implants and detect unsafe loads. This paper presents a frontend sensor circuitry for both small and wide range of loads. A prototype PCB and measurement results with the harvester are demonstrated for small range of loads. These test results validate the proposed approach as PCB test results and circuit simulation results sufficiently match with high accuracy. Simulation results for the wide range of loads are also presented. The front-end electronic circuitry is powered entirely by the harvested power without requiring any external supply or bias voltage. Triboelectric energy harvesting is therefore a promising technique for self-monitoring the load inside knee implants.

Biography

Manav Jain received his B.Tech. degree in electronics and communication engineering from the Aligarh Muslim University (AMU), Aligarh, Uttar Pradesh, India, in 2014 and M.S. degree in electrical engineering from the University of Texas at Dallas (UTD), Richardson, TX, USA, in 2016. In 2016, he started working towards his Ph.D. degree in electrical engineering at the Stony Brook University (SUNY), Stony Brook, NY, USA and graduated in 2021. His research interests include designing low-power frontend electronic circuits and related power management circuits for energy harvesting and implantable applications. Currently, he is working with Intel, Santa Clara as an Analog Design Engineer.

Nabid Aunjum Hossain received his B.S. degree in mechanical engineering from the Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh, in 2016. In 2017 he joined the mechanical engineering department of SUNY Binghamton, Binghamton, NY, USA, where he has been working towards his PhD degree. His research interests are in electro-mechanical systems, vibration and energy harvesting. Currently his focus is on developing a triboelectric energy harvesting based, self-powered instrumented knee implant.

Dr. Shahrzad Towfighian Shahrzad Towfighian received the B.S. degree from the Amirkabir University of Technology, Iran, in 2001, the M.S. degree from Ryerson University, Canada, in 2006, and the Ph.D. degree from the University ofWaterloo, Canada, in 2011. She joined the Mechanical Engineering Department, State University of New York at Binghamton, in Fall 2013. Her research interests are microelectromechanical systems and energy harvesting for bio-medical devices. She focuses on creating theoretical and experimental frameworks to explain the underlying mechanism of electro-mechanical systems. Using these frameworks, she seeks innovative methods to improve functionality of devices for various applications. She was a recipient of several grants from the National Science Foundation and the National Health Institute.

Ryan Willing completed his PhD in Mechanical Engineering at Queen’s University in 2010 and a post-doc at Western University in 2013. He is an Assistant Professor in Mechanical and Materials Engineering and Biomedical Engineering at Western University, a member of Western’s Bone and Joint Institute, and a Scientist with the Lawson Health Research Institute.

Milutin Stanaćević received the B.S. degree in electrical engineering from the University of Belgrade, Belgrade, Serbia, in 1999, and the M.S. and Ph.D. degrees in electrical and computer engineering from Johns Hopkins University, Baltimore, MD, USA, in 2001 and 2005, respectively. In 2005, he joined the Faculty of the Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA, where he is currently an Associate Professor. His current research interests include mixed-signal VLSI circuit design for RF energy harvesting in implantable devices and tag networks, ultralowpower biomedical instrumentation, and acoustic source separation. He was a recipient of the National Science Foundation CAREER Award and the IEEE Region 1 Technological Innovation Award. He was an Associate Editor of the IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS. He also serves on several technical committees of the IEEE Circuits and Systems Society

Emre Salman (Senior Member, IEEE) received the B.S. degree in microelectronics engineering from Sabanci University, Istanbul, Turkey, in 2004, and the M.S. and Ph.D. degrees in electrical engineering from the University of Rochester, Rochester, NY, USA, in 2006 and 2009, respectively. He was previously with STMicroelectronics, Istanbul, Turkey, Synopsys, Mountain View, CA, USA, and Freescale Semiconductor (now NXP Semiconductors), Tempe, AZ, USA, where he was involved in research in the fields of custom circuit design, timing, and noise analysis. Since 2010, he has been with the Department of Electrical and Computer Engineering, Stony Brook University (SUNY), Stony Brook, NY, USA, where he is currently an Associate Professor. His broad research interests include design methodologies for integrated circuits and VLSI systems with applications to low power and secure computing, Internet of Things with energy harvesting, and implantable devices. Dr. Salman was a recipient of the National Science Foundation Faculty Early Career Development Award in 2013, the Outstanding Young Engineer Award from IEEE Long Island, NY, USA, in 2014, and the Technological Innovation Award from the IEEE Region 1 in 2018. He served on the Editorial Board of the IEEE TRANSACTIONS ON VERY LARGE-SCALE INTEGRATION (VLSI) SYSTEMS. He currently serves as the Americas Regional Editor for the Journal of Circuits, Systems and Computers, on the Editorial Board of IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, on the organizational/technical committees of various IEEE and ACM conferences, and as the Chair for the VLSI Systems and Applications Technical Committee (VSA-TC) of the IEEE Circuits and Systems Society.