Instrumentation & Measurement Magazine 24-6 - 54

the mat. Accordingly, a fall is detected with high accuracy
when a long period of inactivity is observed while nobody is
sitting on a chair or lying on a bed.
Challenges of Data Collection and
Analysis
After selecting the appropriate sensors for a HAR system, its
designers must also tackle data collection and analysis, which
has its own challenges that are divided into several categories
[28]. Let us look at some of these challenges for device-free
HAR systems.
Fig. 9. FM24-NP100 microwave ranging sensor.
gives good insight about well-being including neurologic,
muscular and skeletal condition of the user. This has prompted
researchers to design systems for monitoring and analyzing
gait. For example, [17] proposed a micro-doppler based approach
for analyzing and classifying gait patterns for cognitive
assessment in smart homes. To achieve this goal, five different
gait classes are considered including normal, walking, limping
with one or both legs, walking with cane and walking
with cane out of synchronization. Obviously, the patterns created
in walking by humans are periodic, and after two steps,
motions are repeated. In the first step, the collected data is processed
to extract periodic patterns which are not visible in the
time domain but become visible in the second step in the timefrequency
representation (TFR) view by applying Short-Time
Fourier Transform (STFT).
Multi-sensor Fusion
So far, we saw HAR with only one type of sensor, which could
restrict the system to detecting a limited class of activities, or
reducing the detection accuracy due to noise, for instance.
To overcome these limitations, existing approaches use sensor
fusion with different sensor types to better remove error
from noisy data, leading to better detection of activities. For
example, a device-free fall detection system with multi-sensor
fusion is proposed in [26] which utilizes both vibration
and PIR sensors to reduce false alarms. Although the vibration
sensor is reliable for fall detection, some events such as door
slamming, falling objects, or other non-human vibration signals
could cause it to raise a false alarm. To counter that, the
PIR sensor can act as a complement to sense vibrations and
prevent false detection. Another fall detection system using
sensor fusion, proposed in [27], uses both PIR and microwave
motion detectors plus a pressure mat. The PIR sensor is used
for human detection and the microwave motion sensor is used
for object movement detection, while the pressure mat is used
for generating a true logic value when pressure is applied to
54
Feature Extraction
Activities are typically detected by the extracted features
from the data. But different activities may have similar features
which can lead to false detection. Therefore, the features
should be selected in such a way that the activities are highly
distinguishable from each other. In [29], feature extraction is
divided into time and frequency domains. Mean value, standard
deviation, median absolute deviation, minimum and
maximum values, signal magnitude area and interquartile
range are the most common time domain features, while Fast
Fourier Transform (FFT) is one of the commonly used features
in the frequency domain.
Data Labeling
There are a variety of methods to extract features and detect
performed activity. Of these, supervised learning (SL) algorithms
require labeled data for training. Therefore, if SL is
used, it is necessary to first assign labels to the collected data,
which may not only be time-consuming and expensive but
also crucially influenced by the noise and the people performing
the activities. Therefore, to remove the labeling process,
systems can be trained by unsupervised or semi-supervised
learning instead of SL.
Class Imbalance
Because activities such as falling are rare compared to the total
amount of daily activities, collecting data for the former is difficult.
This problem causes an imbalance in the training data:
not having enough data related to the activities of interest. To
address this issue, methods such as using F1-score instead of
accuracy can be used. Accuracy works well when there is balance
between classes, but when we have imbalance, using
F1-score is a better method.
Training and Production Discrepancy
Activities may be performed differently by different users;
also, the users' styles might change over the time. Moreover,
variations in sensors such as type, position and layout in the
environment influence the collected data. Therefore, there exists
a discrepancy between training data collected from one
environment and data produced by another environment. In
machine learning, this discrepancy could be overcome with
transfer learning, which adapts a pre-trained model to a similar
but nonidentical problem.
IEEE Instrumentation & Measurement Magazine
September 2021

Instrumentation & Measurement Magazine 24-6

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