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  • 1.
    Buist, Mirka
    et al.
    Ctr Bion & Pain Res, S-43180 Mölndal, Sweden.;Bion Inst, Melbourne, Vic 3002, Australia.;Univ Gothenburg, Inst Physiol & Neurosci, Sahlgrenska Acad, Dept Physiol, S-41390 Gothenburg, Sweden..
    Damercheli, Shahrzad
    Ctr Bion & Pain Res, S-43180 Mölndal, Sweden.;Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden..
    Truong, Minh Tat Nhat
    KTH, Skolan för teknikvetenskap (SCI), Teknisk mekanik, Farkostteknik och Solidmekanik. Ctr Bion & Pain Res, S-43180 Mölndal, Sweden.
    Sanna, Alessio
    Ctr Bion & Pain Res, S-43180 Mölndal, Sweden..
    Mastinu, Enzo
    Ctr Bion & Pain Res, S-43180 Mölndal, Sweden.;Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden.;St Anna Sch Adv Studies, BioRobot Inst, Artificial Hands Area, I-56025 Pontedera, Italy..
    Ortiz-Catalan, Max
    Ctr Bion & Pain Res, S-43180 Mölndal, Sweden.;Bion Inst, Melbourne, Vic 3002, Australia.;Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden..
    Development and Validation of a Wearable Device to Provide Rich Somatosensory Stimulation for Rehabilitation After Sensorimotor Impairment2023Ingår i: IEEE Transactions on Biomedical Circuits and Systems, ISSN 1932-4545, E-ISSN 1940-9990, Vol. 17, nr 3, s. 547-557Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Training sensory discrimination of the skin has the potential to reduce chronic pain due to sensorimotor impairments and increase sensorimotor function. Currently, there is no such device that can systematically provide rich skin stimulation suitable for a training protocol for individuals with amputation or major sensory impairment. This study describes the development and validation of a non-invasive wearable device meant to repeatedly and safely deliver somatosensory stimulations. The development was guided by a structured design control process to ensure the verifiability and validity of the design outcomes. Two sub-systems were designed: 1) a tactile display for touch and vibration sensations, and 2) a set of bands for sliding, pressure, and strain sensations. The device was designed with a versatile structure that allows for its application on different body parts. We designed a device-paired interactive computer program to enable structured sensory training sessions. Validation was performed with 11 individuals with intact limbs whose upper arm tactile sensitivity was measured over 5 training sessions. Tactile discrimination and perception threshold were measured using the standard 2-point discrimination and Semmes-Weinstein monofilament tests, respectively. The results of the monofilament test showed a significant improvement (p = 0.011), but the improvement was not significant for the 2-point discrimination test(p = 0.141). These promising results confirm the potential of the proposed training to increase the sensory acuity in the upper arms of individuals with intact limbs. Further studies will be conducted to determine how to transfer the findings of this work to improve the pain and/or functional rehabilitation in individuals with sensorimotor impairments.

  • 2.
    Fernández Schrunder, Alejandro
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system.
    Huang, Yu-Kai
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system.
    Rodriguez, Saul
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system.
    Rusu, Ana
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system.
    A Bioimpedance Spectroscopy Interface for EIM Based on IF-Sampling and Pseudo 2-Path SC Bandpass ΔΣ ADC2024Ingår i: IEEE Transactions on Biomedical Circuits and Systems, ISSN 1932-4545, E-ISSN 1940-9990, s. 1-13Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a low-noise bioimpedance (bio-Z) spectroscopy interface for electrical impedance myography (EIM) over the 1 kHz to 2 MHz frequency range. The proposed interface employs a sinusoidal signal generator based on direct-digital-synthesis (DDS) to improve the accuracy of the bio-Z reading, and a quadrature low-intermediate frequency (IF) readout to achieve a good noise-to-power efficiency and the required data throughput to detect muscle contractions. The readout is able to measure baseline and time-varying bio-Z by employing robust and power-efficient low-gain IAs and sixth-order single-bit bandpass (BP) ΔΣ ADCs. The proposed bio-Z spectroscopy interface is implemented in a 180 nm CMOS process, consumes 344.3 - 479.3 μ W, and occupies 5.4 mm 2 area. Measurement results show 0.7 mΩ/√Hz sensitivity at 15.625 kHz, 105.8 dB SNR within 4 Hz bandwidth, and a 146.5 dB figure-of-merit. Additionally, recording of EIM in time and frequency domain during contractions of the bicep brachii muscle demonstrates the potential of the proposed bio-Z interface for wearable EIM systems.

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  • 3.
    Huang, Jiayu
    et al.
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai, China.
    Zhu, Zikai
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai, China.
    Su, Peng
    KTH, Skolan för industriell teknik och management (ITM), Maskinkonstruktion.
    Chen, DeJiu
    KTH, Skolan för industriell teknik och management (ITM), Maskinkonstruktion, Mekatronik och inbyggda styrsystem.
    Zheng, Li-Rong
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai, China.
    Zou, Zhuo
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai, China.
    A Reconfigurable Near-Sensor Processor for Anomaly Detection in Limb Prostheses2024Ingår i: IEEE Transactions on Biomedical Circuits and Systems, ISSN 1932-4545, E-ISSN 1940-9990, s. 1-14Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a reconfigurable near-sensor anomaly detection processor to real-time monitor the potential anomalous behaviors of amputees with limb prostheses. The processor is low-power, low-latency, and suitable for equipment on the prostheses and comprises a reconfigurable Variational Autoencoder (VAE), a scalable Self-Organizing Map (SOM) Array, and a window-size-adjustable Markov Chain, which can implement an integrated miniaturized anomaly detection system. With the reconfigurable VAE, the proposed processor can support up to 64 sensor sampling channels programmable by global configuration, which can meet the anomaly detection requirements in different scenarios. A scalable SOM array allows for the selection of different sizes based on the complexity of the data. Unlike traditional time accumulation-based anomaly detection methods, the Markov Chain is utilized to detect time-series-based anomalous data. The processor is designed and fabricated in a UMC 40-nm LP technology with a core area of 1.49 mm2 and a power consumption of 1.81 mW. It achieves real-time detection performance with 0.933 average F1 Score for the FSP dataset within 24.22 s, and 0.956 average F1 Score for the SFDLA-12 dataset within 30.48 s, respectively. The energy dissipation of detection for each input feature is 43.84 nJ with the FSP dataset, and 55.17 nJ with the SFDLA-12 dataset. Compared with ARM Cortex-M4 and ARM Cortex-M33 microcontrollers, the processor achieves energy and area efficiency improvements ranging from 257×, 193× and 11×, 8×, respectively. IEEE

  • 4.
    Ivanisevic, Nikola
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system, Integrerade komponenter och kretsar.
    Rodriguez, Saul
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system, Integrerade komponenter och kretsar.
    Rusu, Ana
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system, Integrerade komponenter och kretsar.
    Impedance Spectroscopy Based on Linear System Identification2019Ingår i: IEEE Transactions on Biomedical Circuits and Systems, ISSN 1932-4545, E-ISSN 1940-9990, Vol. 13, nr 2, s. 396-402Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Impedance spectroscopy is a commonly used mea-surement technique for electrical characterization of a sample-under-test over a wide frequency range. Most measurementmethods employ a sine wave excitation generator, which implies apoint-by-point frequency sweep and a complex readout architec-ture. This paper presents a fast, wide-band, measurement methodfor impedance spectroscopy based on linear system identification.The main advantage of the proposed method is the low hardwarecomplexity, which consists of a 3-level pulse waveform, aninverting voltage amplifier and a general purpose ADC. A proof-of-concept prototype, which is implemented with off-the-shelfcomponents, achieves an estimation fit of approximately 96%.The prototype operation is validated electrically using knownRC component values and tested in real application conditions.

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  • 5.
    Rodriguez, Saul
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Integrerade komponenter och kretsar.
    Ollmar, S.
    Waqar, M.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Integrerade komponenter och kretsar.
    Rusu, Ana
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Integrerade komponenter och kretsar.
    A Batteryless Sensor ASIC for Implantable Bio-Impedance Applications2015Ingår i: IEEE Transactions on Biomedical Circuits and Systems, ISSN 1932-4545, E-ISSN 1940-9990Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The measurement of the biological tissue’s electrical impedance is an active research field that has attracted a lot of attention during the last decades. Bio-impedances are closely related to a large variety of physiological conditions; therefore, they are useful for diagnosis and monitoring in many medical applications. Measuring living tissues, however, is a challenging task that poses countless technical and practical problems, in particular if the tissues need to be measured under the skin. This paper presents a bio-impedance sensor ASIC targeting a battery-free, miniature size, implantable device, which performs accurate 4-point complex impedance extraction in the frequency range from 2 kHz to 2 MHz. The ASIC is fabricated in 150 nm CMOS, has a size of 1.22 mm × 1.22 mm and consumes 165 μA from a 1.8 V power supply. The ASIC is embedded in a prototype which communicates with, and is powered by an external reader device through inductive coupling. The prototype is validated by measuring the impedances of different combinations of discrete components, measuring the electrochemical impedance of physiological solution, and performing ex vivo measurements on animal organs. The proposed ASIC is able to extract complex impedances with around 1 Ω resolution; therefore enabling accurate wireless tissue measurements.

  • 6.
    Wang, Deyu
    et al.
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
    Xu, Jiawei
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system. Guangdong Institute of Intelligence Science and Technology, Zhuhai 519115, China.
    Li, Feng
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
    Zhang, Lianhao
    Department of Electrical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark.
    Cao, Chengwei
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
    Stathis, Dimitrios
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system.
    Lansner, Anders
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Beräkningsvetenskap och beräkningsteknik (CST).
    Hemani, Ahmed
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknik, Elektronik och inbyggda system.
    Zheng, Li Rong
    Guangdong Institute of Intelligence Science and Technology, Zhuhai 519115, China; School of Information Science and Technology, Fudan University, Shanghai 200437, China.
    Zou, Zhuo
    State Key Laboratory of Integrated Chips and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
    A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning2023Ingår i: IEEE Transactions on Biomedical Circuits and Systems, ISSN 1932-4545, E-ISSN 1940-9990, Vol. 17, nr 5, s. 1153-1165Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The memristor has been extensively used to facilitate the synaptic online learning of brain-inspired spiking neural networks (SNNs). However, the current memristor-based work can not support the widely used yet sophisticated trace-based learning rules, including the trace-based Spike-Timing-Dependent Plasticity (STDP) and the Bayesian Confidence Propagation Neural Network (BCPNN) learning rules. This paper proposes a learning engine to implement trace-based online learning, consisting of memristor-based blocks and analog computing blocks. The memristor is used to mimic the synaptic trace dynamics by exploiting the nonlinear physical property of the device. The analog computing blocks are used for the addition, multiplication, logarithmic and integral operations. By organizing these building blocks, a reconfigurable learning engine is architected and realized to simulate the STDP and BCPNN online learning rules, using memristors and 180 nm analog CMOS technology. The results show that the proposed learning engine can achieve energy consumption of 10.61 pJ and 51.49 pJ per synaptic update for the STDP and BCPNN learning rules, respectively, with a 147.03× and 93.61× reduction compared to the 180 nm ASIC counterparts, and also a 9.39× and 5.63× reduction compared to the 40 nm ASIC counterparts. Compared with the state-of-the-art work of Loihi and eBrainII, the learning engine can reduce the energy per synaptic update by 11.31× and 13.13× for trace-based STDP and BCPNN learning rules, respectively.

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