Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

Electrical impedance spectroscopy (EIS) is widely recognized as a powerful tool in biomedical research. For example, it allows detection and monitoring of diseases, measuring of cell density in bioreactors, and characterizing the permeability of tight junctions in barrier-forming tissue models. However, with single-channel measurement systems, only integral information is obtained without spatial resolution. Here we present a low-cost multichannel impedance measurement set-up capable of mapping cell distributions in a fluidic environment by using a microelectrode array (MEA) realized in 4-level printed circuit board (PCB) technology including layers for shielding, interconnections, and microelectrodes. The array of 8 × 8 gold microelectrode pairs was connected to home-built electric circuitry consisting of commercial components such as programmable multiplexers and an analog front-end module which allows the acquisition and processing of electrical impedances. For a proof-of-concept, the MEA was wetted in a 3D printed reservoir into which yeast cells were locally injected. Impedance maps were recorded at 200 kHz which correlate well with the optical images showing the yeast cell distribution in the reservoir. Blurring from parasitic currents slightly disturbing the impedance maps could be eliminated by deconvolution using an experimentally determined point spread function. The MEA of the impedance camera can in future be further miniaturized and integrated into cell cultivation and perfusion systems such as organ on chip devices to augment or even replace light microscopic monitoring of cell monolayer confluence and integrity during the cultivation in incubation chambers.

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