component and vibration component in the raw reading, an inertial sensor
fusion is a promising solution. Some other works explored sensing fusion to
find the contribution of capacitive sensing (Reinschmidt et al., 2022; Bian et
al., 2022b, d). In (Reinschmidt et al., 2022), the charge sensing module was
demonstrated to be able to improve the accuracy of IMU-alone hand gesture
recognition by more than 10%; In (Bian et al., 2022b), the body capacitance
variation information supplied better results over the IMU for workouts
counting(0.800 vs. 0.756 when wearing the sensors on the wrist), and improved
the recognition of collaborative activities with an F-score over a single
wrist accelerometer approach by 16%. As can be seen, fusing capacitive sensing
and inertial measurement sensing is meaningful and promising, especially in
motion pattern recognition scenarios.

Table 2 listed the crucial characteristics of the IMU sensor and body-area
capacitive sensor when targeting wearable motion sensing. Both of them have a
small form factor to fit into wearable devices. At the same time, the body
capacitive sensor outperforms in cost (nothing more than a few discrete
components and an amplifier), power consumption (with reference from
LSM6DSV16X), attach position (full-body motion sensing ability for body
capacitive sensor, while the IMU has to be deployed on the moving body part),
and sensing field (being able to sense the surrounding variation). However,
the body capacitive sensing is weak in robustness, which is crucial for a
reliable application. Regarding the circuit form, the IMU sensor has long been
in the form of a tiny MEMS structure, while capacitive sensors are primarily
in discrete form when using the displacement current as the signal source. The
only commercially available integrated charged-based capacitive chip is the
Qvar-integrated MEMS sensors (fused with inertial sensor and barometer sensor)
from ST Microelectronics released in 2022, as described before. This will
bring impressive results in exploring body capacitive-based motion and
presence sensing in a sensor fusion approach for robust results.

Besides the fusion with inertial sensors, the body-area capacitive sensing
could also be used as a triggering signal for other sensors like camera, in
some edge, battery-powered embedded systems that require long-term
environmental monitoring, taking advantage of ultra-low power consumption and
meter-level intrusion sensing range characters of body-area capacitive
sensors. With proper configuration, an instrumented capacitive sensor can
sense the proximity of a human body in meters away with a power level $uW$.

###  6.4. Subject-dependent Continuous Learning

As mentioned in 5.2, the generation of human activity recognition-based
capacitive sensing is still a tough problem because of the subject and
environment diversity. Most of the traditional offline machine learning
methods are often designed to learn a model from the entire training data set
at once (Hoi et al., 2021), which means that there will not be new knowledge
that can be obtained by this trained model in the future. Therefore, the
recognition accuracy could decay over time due to the variation in environment
and users’ behavior in real-life application scenarios. To keep a reliable
activity accuracy for all users over a long time, subject-dependent computing
is an important work direction for practical real-world applicability of
capacitive sensing-based technology. Online learning and incremental learning
provide a promising solution for subject-dependent computing, as the model
based on online learning has the ability to update the model from new data
continually regarding different subjects and environments. Online learning
solution consists of two main components: model inference and model training.
The model training process is often performed if the recognition accuracy
degradation is detected or new activity is added. For example, in (Yu et al.,
2021) the authors proposed a personalized federated HAR framework and
developed an unsupervised gradient aggregation strategy to overcome the
concept drift and convergence instability issues in online federated learning
process. The proposed framework demonstrated an additional 10% improvement
across all metrics on average. In (Disabato and Roveri, 2022), the authors
introduced a tiny machine learning model for concept drift (TML-CD) solution
based on deep learning feature extractors and a k-nearest neighbors (k-NNs)
classifier. Three adaptation mechanisms, such as passive update, active
update, and hybrid update were employed to correct the KNN classifier. In
active update mechanisms, the adaptations were carried out at each new
incoming supervised sample without detection of an accuracy change. There is a
change detection test in active mechanisms, the update process was executed
when a change is detected. The hybrid mechanism is a combination of the first
two. Noteworthy, the proposed solution can be deployed on a resource-
constrained microcontroller. Different from online learning based on the
classical machine learning method, the training process of the model based on
deep learning techniques requires much more computation and memory resources
than inference, which is a considerable challenge when deploying the online
learning model on edge devices(on-device learning). Usually, the training
process is completed on the cloud (Awan et al., 2015; Chen et al., 2017).
However, the data exchange can lead to high power consumption and privacy
issues. Training on edge devices is becoming a promising method for online
learning to overcome these above problems. To overcome the resource-
constrained problem of on-device learning, many solutions were proposed, like
reducing the number of trainable parameters (Han et al., 2015; Cai et al.,
2019) and reducing the activations (Cai et al., 2020). Recently, an algorithm-
system co-design framework to make on-device training possible with only 256KB
of memory was proposed in (Lin et al., 2022). The framework enables IoT
devices not only to perform inference but also to continuously adapt to new
data for on-device lifelong learning, which will be an important cornerstone
for subject-dependent continuous learning on edge devices.

In the case of body-area electric field sensing, most of the data in the
existing plentiful explorations are processed with supervised classical
machine learning or deep neural networks methods, which means the trained
model parameters are fixed without considering future data shift caused by
environmental variation or unregistered users. As body-area electric field
sensor data is highly personalized, online learning with an incremental
training mechanism could make use of the incrementally collected unlabeled
user samples to actively adjust the model parameters. Such an adaptive online
learning method will benefit most of the model-based exploration in the field
of HCI and HAR.

##  7\. Conclusion

Body-area capacitive/electric field sensing is a promising sensing modality
for human activity recognition and human-computer interaction backed by the
property that the body is an ideal conductor. Due to this feature, the body
can, first, radiate a passive electric field from the body to the
surroundings, and second, deform an existing electric field in an instrumented
environment. Previous works based on this property have shown a significant
increasing trend in the last decade, including motion sensing, physiological
sensing, assisted living, entertainment, etc. Some designs have reported 100%
activity recognition accuracy (for example, in the seat posture detection
application), and a uW-level of power consumption for wearable motion sensing
(for example, in the task of workouts recognition), which is over ten times
power saving than the traditional wearable motion sensor IMU. However, despite
the pervasive exploration of the body-area electric field, a comprehensive
summary doesn’t so far exist for an enlightening guideline. This paper fills
in the gap by comprehensively summarizing the existing related works so that
researchers can have a systematic overview of the current exploration status
of body-area capacitive sensing. For this purpose, we categorized the topic
into three classes: body-part capacitive sensing, whole-body capacitive
sensing, and body-to-body capacitive sensing, and enumerated broadly the
published works within each category. An in-depth description of the
underlying technical tricks is given, including hardware implementations,
applied algorithms, targeted applications, performance, and limitations.
Especially, we summarized the sensing source signals for body-area capacitive
applications, namely the time, frequency, and current. We also discussed their
suited usage scenarios, aiming to provide essential information so that other
researchers can decide if and in what capacitive sensing form is suitable for
their specific applications. Finally, we analyzed the challenges for the
massive deployment of the body-area capacitive sensing technique in practical
applications like the weakness in robustness, and proposed several directions
for a more accurate, robust, easy-of-use sensing with body-area capacitance,
aiming to encourage researchers for further novel investigations considering
the pervasive and promising usage scenarios backed by body-area capacitive
sensing.

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##  Appendix A History

Figure 15. History of the capacitor forms, capacitive based sensors and the
development of capacitive based application (body-area capacitive sensing in
bold form)

Capacitive sensor was born in the pioneering days of electricity and was used
to store the charge in the form of Leyden Jar (Heilbron, 1966), as electricity
was thought to be fluid in the 18th century. In the 1830s, Michael Faraday did
experiments that determined that the material between the capacitor’s plates
had an effect on the quantity of charge on the capacitor’s plates. For this
work, the unit for capacitance is called the farad. In the late 19th century,
paper capacitors and electrolytic capacitors were invented and were used from
the early 20th century, for example, as decoupling capacitors in telephony.
During the second and third industrial revolutionaries, different forms of
capacitors were invented to satisfy the rapid development of industry, like
the ceramic capacitor used in the resonant circuit and the tantalum capacitor
used as a reliable low-voltage support capacitor to complement the newly
invented transistor. Meanwhile, capacitive-based sensors were explored to
measure the height (height gauge), pressure (manometer), displacement
(displacement gauge), etc. From the early 20th century, plentiful capacitive-
based sensors were developed and optimized to sense the physical world,
especially driven by the silicon industry and the MEMS technique (Bogue,
2007). With the development of capacitive sensors, a rich amount of
capacitive-based applications were explored, like the measurement of the
thickness of thin water sheets (Black, 1959), liquid level (Wilner, 1960),
humidity (Misevich, 1969), displacement (Todd, 1954), etc. Meanwhile, the
body-area capacitive sensing also started to be explored, like the capacitive
blood pressure manometer (Tompkins, 1949), which senses the capacitance
variation caused by the varied blood pressure in the arteries. The first body
capacitance-enabled consumer device is the special music instrument Theremin
(Nikitin, 2012), invented by the 23-year-old Leon Theremin in 1919 by
accident. The story of Theremin, as a key milestone, has been widely described
in body-area electric field-related works in the background and historical
sections (Smith, 1996; Grosse-Puppendahl et al., 2017; Braun et al., 2015b).
”He was working in a laboratory in Russia as a young scientist, and he was
actually working on a gas meter to measure the density of gases, so as he
brought his hand closer to the gas meter, he heard kind of a higher squeal.
And as he brought his hand back to his body and away from the machine, it was
a slower squeal.” wrote Glinsky in (Glinsky, 2000). Just as in the pioneering
days of aviation, when humans made their own planes out of wood and canvas and
struggled to leap into the air, not understanding enough about aerodynamics to
know how to stay there, Theremin experienced the same period. With decades of
development, electrical Theremin was invented (Kuik, 2004), and the behind
principle has been evidently verified (Skeldon et al., 1998): the human body
capacitance (or the static body electric field), namely the capacitance
between the human body and the surroundings. The two metal antennas on the
instrument sense the relative position of the thereminist’s hands and control
oscillators for frequency with one hand and amplitude with the other. The
electric signals from the theremin are amplified and sent to a loudspeaker.
Similar body area capacitance or body-area electric field could also be found
in special fishes like the gymnarchus niloticus (Lissmann and Machin, 1958;
Bullock, 1982) with a more sensitive way: the potential distribution over the
surface of the fish is detected by a series of receptors; this information is
then interpreted to indicate the position of objects with a conductivity
differing from that of water. In 1965, E.A. Johnson invented what is generally
considered the first finger-driven capacitive touchscreen (Johnson, 1965),
utilizing the conductance/capacitance property of the finger. Besides that,
researchers and engineers also explored body capacitance-based respirometer
(Barrow and Vorwald, 1969), the limb loads exerted by patients (Miyazaki et
al., 1986), etc.. The body-area capacitance measurement (Jonassen, 1998) and
isolation method (Forster, 1974) for patient protection were also
investigated, as in the patient environments, the presence of electrostatic
fields arising from unshielded wiring will result in an isolated patient
exhibiting capacitance between the earth and the active leads.

Starting from the 21st century, body-area capacitive sensing showed an
explosive development benefitting from its pervasiveness and the emerging
novel and high-precision sensing approaches, such as the resonator-based
capacitance chips like FDC2x1x from Texas Instruments(TI), and the charger
variation-based capacitance chips like QVAR from STMicroelectronics. Since
capacitive sensing is a low-power, low-cost, contactless sensing technique, it
was applied to applications like biophysical signal monitoring (Yama et al.,
2007), position tracking (Osoinach, 2007), activity classification (Cheng et
al., 2010), and intrabody communication (Shinagawa et al., 2004), etc. As a
summary, Fig. 15 depicts the history of capacitance development, including the
different capacitor forms, capacitive-based sensors, and their backed
applications, with body-area applications in bold format.